An Empirical Study on the Relation between Programming Languages and the Emergence of Community Smells.*

Abstract. To provide a measurable representation of social issues in software teams, the research community defined a set of anti-patterns that may lead to the emergence of both social and technical debt, i.e., "community smells". Researchers have investigated community smells from different perspectives; in particular, they have analyzed how product-related aspects of software development, such as architecture and introducing a new language, could influence community smells. However, how technical project characteristics may be in relation to the emergence of community smells is still unknown. Different from those works, we aim to investigate how adopting specific programming languages might influence the socio-technical alignment and congruence of the development community, possibly inducing their overall ability to communicate and collaborate, leading to the emergence of social anti-patterns, i.e., community smells. We studied the relationship between the most used programming languages and the community smells in 100 open-source projects on GitHub. Key results of the study show a low statistical correlation for specific community smells like Prima Donna Effects, Solution Defiance, and Organizational Skirmish, highlighting the fact that for some programming languages, its adoption could not be an indicator of the presence or absence of community smells.

AGORA: An Approach for Generating Acceptance Test Cases from Use Cases.*

Abstract. This paper introduces AGORA, an innovative approach that leverages Large Language Models to automate the definition of acceptance test cases from use cases. AGORA consists of two phases that exploit prompt engineering to 1) identify test cases for specific use cases and 2) generate detailed acceptance tests cases. AGORA was evaluated through a controlled experiment involving industry professionals, comparing the effectiveness and efficiency of the proposed approach with the manual method. The results showed that AGORA can generate acceptance test cases with a quality comparable to that obtained manually but improving the process efficiency by over 90% in a fraction of the time. Furthermore, user feedback indicated high satisfaction with using the proposed approach. These findings underscore the potential of AGORA as a tool to enhance the efficiency and quality of the software testing process.

Collecting and Implementing Ethical Guidelines for Emotion Recognition in an Educational Metaverse.*

Abstract. The metaverse represents a persistent, online 3D universe where people can interact, socialize, and work toward common goals. Education represents a key application domain, as it has the potential to enhance experiential learning and collaboration between learners and between learners and educators. However, challenges to the widespread adoption of educational metaverses persist. This paper focuses on emotional isolation, i.e., the feeling of emotional disconnection or loneliness, which can hinder learners' motivation and participation. Machine learning-enabled emotional recognition systems have the potential to address this challenge, offering educators with feedback on the emotional states of learners within the metaverse. Yet, the integration of emotion recognition systems raises ethical concerns regarding consent, privacy, and algorithmic bias. In this paper, we first conduct a literature review on the ethical considerations surrounding the deployment of emotion recognition technology within educational metaverses. Then, we report on the implementation of these guidelines within SENEM, an educational metaverse platform available in the literature. Through this research, we aim to contribute to the responsible deployment of emotion recognition technology in educational settings, ultimately fostering a supportive and inclusive learning environment for all learners.

Is Attention All You Need? Toward a Conceptual Model for Social Awareness in Large Language Models.*

Abstract. Large Language Models (LLMs) are revolutionizing the landscape of Artificial Intelligence (AI) due to recent technological breakthroughs. Their remarkable success in aiding various Software Engineering (SE) tasks through AI-powered tools and assistants has led to the integration of LLMs as active contributors within development teams, ushering in novel modes of communication and collaboration. However, great power comes with great responsibility: ensuring that these models meet fundamental ethical principles such as fairness is still an open challenge. In this light, our vision paper analyzes the existing body of knowledge to propose a conceptual model designed to frame ethical, social, and cultural considerations that researchers and practitioners should consider when defining, employing, and validating LLM-based approaches for software engineering tasks.

Unmasking Data Secrets: An Empirical Investigation into Data Smells and Their Impact on Data Quality.*

Abstract. Artificial Intelligence (AI) is rapidly advancing with a data-centered approach suitable for various domains. Nevertheless, AI faces significant challenges, particularly in data quality. Data collection from diverse sources can introduce quality issues that may threaten the development of AI-enabled systems. A growing concern in this context is the emergence of data smells – issues specific to the data used in building AI models, which can have long-term consequences. In this paper, we aim at enlarging the current body of knowledge on data smells, by proposing a two-step investigation into the matter. First, we updated an existing literature review in an effort of cataloguing the currently existing data smells and the tools to detect them. Afterward, we assess the prevalence of data smells and their correlation with data quality metrics. We identify a novel set composed of 12 data smells distributed across three additional categories. Secondly, we observe that the correlation between data smells and data quality is notably impactful, exhibiting a pronounced and substantial effect, especially in highly diffused data smell instances. This research sheds light on the complex relationship between data smells and data quality, providing valuable insights into the challenges of maintaining AI-enabled systems.

ReFAIR: Toward a Context-Aware Recommender for Fairness Requirements Engineering.*

Abstract. Machine learning (ML) is increasingly being used as a key component of most software systems, yet serious concerns have been raised about the fairness of ML predictions. Researchers have been proposing novel methods to support the development of fair machine learning solutions. Nonetheless, most of them can only be used in late development stages, e.g., during model training, while there is a lack of methods that may provide practitioners with early fairness analytics enabling the treatment of fairness throughout the development lifecycle. This paper proposes ReFair, a novel context-aware requirements engineering framework that allows to classify sensitive features from User Stories. By exploiting natural language processing and word embedding techniques, our framework first identifies both the use case domain and the machine learning task to be performed in the system being developed; afterward, it recommends which are the context-specific sensitive features to be considered during the implementation. We assess the capabilities of ReFair by experimenting it against a synthetic dataset---which we built as part of our research---composed of 12,401 User Stories related to 34 application domains. Our findings showcase the high accuracy of ReFair, other than highlighting its current limitations.

SERGE – Serious Game for the Education of Risk Management in Software Project Management.*

Abstract. Software Project Management is the systematic and disciplined approach for planning, executing, monitoring, controlling, and closing software development projects. It plays a critical role in the success of software projects and encompasses several processes for ensuring the successful completion of a software project. Among them, risk management emerges as a critical pivot to be able to react to the unpredictable events that often affect software projects. Teaching risk management is vital to equip individuals and organi- zations with the adapted skills to prevent and monitor challenges and potential issues. In this paper, we propose a serious game named Serge, conceived to involve students in learning risk management and improve their skills through gamification and simulation of a real-world application context. The features for the design of Serge were identified through a literature review. An iterative Game Design Phase was employed to build, test, and refine the design of Serge. Finally, the proposed approach was assessed by conducting a controlled experiment to compare risk management skills acquired through a traditional lecture and using Serge. The results show that adopting a serious game as Serge, able to involve the students actively, can improve the acquisition of risk management skills.

Dealing With Cultural Dispersion: a Novel Theoretical Framework for Software Engineering Research and Practice.*

Abstract. Software development is fundamentally a team-driven process; researchers in software engineering have identified various human and social factors that can significantly impact it. Culture emerged as a critical element, and the diversity deriving from cultural differences can be highly impactful both positively and negatively. Despite existing knowledge about how culture influences software development, limitations persist. Most importantly, a unified and comprehensive (grounded) theory of how cultural differences influence and are managed in software development has yet to exist. This lack has two significant consequences: (1) it makes research on culture fragmented, leading to the continual definition of new concepts that do not allow state of the art to advance significantly, and (2) it reduces the ability of the research to be transferred to practitioners since there is no framework designed to be understood and used by them. To address the above-mentioned limitation, this work proposed a theoretical framework of "Dealing With Cultural Dispersion", which focuses on challenges and benefits originating from cultural differences and strategies for dealing with them. Such a framework was developed through a qualitative study using an iterative research approach, including interviews and socio-technical grounded theory for data analysis. The proposed framework was designed to reveal the tangible effects of practitioners' culture in software development, allowing software teams to (1) clearly understand the problem and (2) implement the correct strategy for addressing it. Additionally, researchers can use this framework as a foundation to (deductively) develop a more robust and comprehensive theory in this field.

Automating Test-Specific Refactoring Mining: A Mixed-Method Investigation.*

Abstract. Refactoring is a practice commonly used by developers to restructure the source code without changing its external behavior. Over the last decades, the software engineering research community has been making use of mining software repository techniques to investigate refactoring under multiple perspectives, identifying properties and impact of this practice on source code quality, other than using refactoring data coming from software repositories to build automated recommendation systems. While the current state of the art proposes various automated tools to mine refactoring data, there is still a lack of instruments that may help researchers when mining test-specific refactoring data. The availability of those instruments may enable additional, specialized techniques to support developers while refactoring test code. In this paper, we introduce an approach that extends RefactoringMiner - a well-established refactoring mining tool having high precision and recall scores — and is able to detect seven test-specific refactoring operations. We perform mixed-method research to assess capabilities and usefulness of the approach. First, we compare the test-specific refactoring data extracted by the approach against an oracle of 375 test-specific refactorings. Second, we engage with 15 software engineering researchers and apply a technology acceptance model to investigate how they would benefit from our approach. The key results of the study show that our approach reaches 100% and 92.5% of precision and recall scores, respectively. In addition, the approach is considered useful and suitable for various research tasks, including the definition of novel learning models able to recommend test-specific refactoring actions.

Please, Be Realistic! An Empirical Study on the Performance of Vulnerability Prediction Models.*

Abstract. Software vulnerabilities are infamous threats to the security of computing systems, and it is vital to detect and correct them before releasing any piece of software to the public. Many approaches for the detection of vulnerabilities have been proposed in the literature; in particular, those leveraging machine learning techniques, i.e., vulnerability prediction models, seem quite promising. However, recent work has warned that most models have only been evaluated in in-vitro settings, under certain assumptions that do not resemble the real scenarios in which such approaches are supposed to be employed. This observation ignites the risk that the encouraging results obtained in previous literature may be not as well convenient in practice. Recognizing the dangerousness of biased and unrealistic evaluations, we aim to dive deep into the problem, by investigating whether and to what extent vulnerability prediction models' performance changes when measured in realistic settings. To do this, we perform an empirical study evaluating the performance of a vulnerability prediction model, configured with three data balancing techniques, executed at three different degrees of realism, leveraging two datasets. Our findings highlight that the outcome of any measurement strictly depends on the experiment setting, calling researchers to take into account the actuality and applicability in practice of the approaches they propose and evaluate.

Understanding Developer Practices and Code Smells Diffusion in AI-Enabled Software: A Preliminary Study.*

Abstract. To deal with continuous change requests and the strict time-to-market, practitioners and big companies constantly update their software systems to meet users' requirements. This practice force developers to release immature products, neglecting best practices to reduce delivery times. As a possible result, technical debt can arise, i.e., potential design issues that can negatively impact software maintenance and evolution and, in turn, increase both the time-to-market and costs. Code smells, i.e., sub-optimal design decisions identifiable by computing software metrics and providing a general overview of code quality, are common symptoms of technical debt. While previous research focused on code smells primarily considering them in the context of Java, the growing popularity of Python, particularly for developing artificial intelligence (AI)-Enabled systems, calls for additional investigations. This preliminary analysis addresses this gap by exploring the diffusion of Python-specific code smells, and the activities performed by developers that induce the introduction of code smells in their systems. To perform our preliminary investigation, we selected 200 AI-Enabled systems available in the Niche dataset; We extracted 10,611 information on the releases using PyDriller, and PySmell to extract information about code smells. The results reveal several insights: 1) Code smells related to object-oriented principles are rarely detected in Python; 2) Complex List Comprehension is the most prevalent and the most long-alive smell; 3) The main activities that can induce code smells are evolutionary. This study fills a critical gap in the literature by providing empirical evidence on the evolution of code smells in Python-based AI-enabled systems.

The Yin and Yang of Software Quality: On the Relationship between Design Patterns and Code Smells.*

Abstract. Software reuse is considered the silver bullet of software engineering. It has been largely demonstrated that the proper implementation of design and reuse principles can substantially reduce the effort, time, and costs required to develop software systems. Design patterns are one of the most affirmed techniques for source code reuse. While previous work pointed out their benefits in terms of maintainability and understandability, some seem to raise the opposite concern, suggesting that they can negatively impact code quality from the developers' perspectives. We recognize such discrepancy in the literature, and we aim to fill this gap by investigating whether and how design patterns are related to the emergence of issues compromising code understandability, namely the Complex Class, God Class, and Spaghetti Code smells, which have been also shown to increase the change- and fault-proneness of code. We perform an empirical evaluation on 15 Java projects evolving over 542 releases, and we find that, although design patterns are supposed to improve code quality without prejudice, they can be related to dangerous issues, as we observe the emergence of code smells in the classes participating in their implementation. From our findings, we distill a number of implications for developers and project managers to support them in dealing with design patterns.

Toward a Secure Educational Metaverse: A Tail of Blockchain Design for Educational Environments.*

Abstract. In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persistent, virtual, three-dimensional environment that is supposed to address most of the limitations of 2D information technologies. Nonetheless, there are still software engineering challenges to face to enable such a metaverse, especially when turning to software security and privacy. In this paper, we aim at performing the first steps toward an improved understanding of the security perspective of educational metaverse, by analyzing how blockchain can be employed within educational environments and how applications may be designed. Our ultimate goal is to provide insights into how blockchain can be further tailored in the context of educational metaverse. We conduct a systematic literature review, which targets 20 primary studies. The key findings of the study showcase the use of blockchain in 3 educational tasks, other than describing the blockchain design approaches, which protocol they commonly use and the associated limitations. We conclude by developing a conceptualization of a blockchain-based educational metaverse.

Meet C4SE: Your New Collaborator for Software Engineering Tasks.*

Abstract. The software industry has rapidly increased in complexity and scale, leading to challenges in managing in- formation and tasks among developer teams, often resulting in inefficiencies, misunderstandings, and delays. Moreover, the increasing search for automated tasks led to the extensive adoption of chatbots—a.k.a. conversational agents—for software development purposes. However, despite their undoubted positive contributions, practitioners started to identify numerous issues deriving from their adoption, both technical and social, first of which, the uselessness of the provided support due to the bot’s lack of full working context. To address such a limitation, we propose C4SE, a chatbot designed to assist software engineers and managers in performing several tasks. The idea behind the bot is to collect information from the different tasks that could be useful for others to provide better support and tailor the bot to the specific operational context-i.e., the development team using it. To enable such task heterogeneity and contextual persistence, we operationalize the GPT 3.5 model for understanding the user’s intent and a specialized data store based on a vector database for long-term memory for maintaining contextual information. With these characteristics, C4SE can provide benefits to the entire software development lifecycle increasing practitioners' productivity. We presented a prototype of the tool able to perform code suggestions, code reviews, GitHub API operationalization, and unit and acceptance test case generation. A preliminary evaluation was carried out reporting encouraging results.

ECHO: An Approach to Enhance Use Case Quality Exploiting Large Language Models.*

Abstract. UML use cases are commonly used in software engineering to specify the functional requirements of a system since they are an effective tool for interacting with stakeholders thanks to the use of natural languages. However, producing high- quality use cases can be challenging due to the lack of precise guidelines and suitable tools. This can lead to problems, e.g. inaccuracy and incompleteness, in the derived software artifacts and the final product. Recent advancements in Natural Language Processing and Large Language Models (LLMs) can provide the premises for developing tools supporting activities based on natural languages. In this paper, we propose ECHO, a novel approach for supporting software engineers in enhancing the quality of UML use cases using LLMs. Our approach consists of a co-prompt engineering approach and an iterative and interactive process with the LLM to improve the quality of use cases, based on practitioners’ feedback. To prove the feasibility of the proposal, we instantiated the approach using ChatGPT and performed a controlled experiment to assess its effectiveness by involving seven software engineering professionals. Three were part of the experimental group and used ECHO to improve the quality of the use cases. Three others were the control group and enhanced the quality of use cases manually. Finally, the last participant acted as an oracle, blind w.r.t. the groups, and evaluated the quality of the enhanced use cases, both qualitatively by means of a questionnaire, and quantitatively, by means of the Use Case Points metric. Results show that ECHO can effectively support software engineers to improve use cases’ quality thanks to the prompts suitably designed to interact with ChaGPT.

Security Testing in the Wild: An Interview Study.*

Abstract. Modern software systems are increasingly complex and the risk of falling into security concerns is high if these systems are not developed with a proper security mindset. Despite the empirical studies and security-oriented approaches proposed by researchers and tool vendors, we still point out a lack of knowledge on the security testing processes applied by companies to reduce risks connected to software security. In this paper, we aim to bridge this gap of knowledge by performing an interview-based study with 19 security experts to understand how companies arrange security testing and how the process of security testing is actually performed in practice. Our results highlight that some companies incorporated the figure of the security tester in the software life cycle, yet practitioners reported a lack of standardized guidelines for security testing. From a management perspective, our results suggest that the introduction of formal communication between development and security testing teams may lead to better performance.

"There and Back Again?" On the Influence of Software Community Dispersion Over Productivity.*

Abstract. Estimating and understanding productivity still represents a crucial task for researchers and practitioners. Researchers spent significant effort identifying the factors that influence software developers' productivity, providing several approaches for analyzing and predicting such a metric. Although different works focused on evaluating the impact of human factors on productivity, little is known about the influence of cultural/geographical diversity in software development communities. Indeed, in previous studies, researchers treated cultural aspects like an abstract concept without providing a quantitative representation. This work provides an empirical assessment of the relationship between cultural and geographical dispersion of a development community---namely, how diverse a community is in terms of cultural attitudes and geographical collocation of the members who belong to it---and its productivity. To reach our aim, we built a statistical model that contained product and socio-technical factors as independent variables to assess the correlation with productivity, i.e., the number of commits performed in a given time. Then, we ran our model considering data of 25 open-source communities on GitHub. Results of our study indicate that cultural and geographical dispersion impact productivity, thus encouraging managers and practitioners to consider such aspects during all the phases of the software development lifecycle.

A Multivocal Literature Review of MLOps Tools and Features.*

Abstract. DevOps has become increasingly widespread, with companies employing its methods in different fields. In this context, MLOps automates Machine Learning pipelines by applying DevOps practices. Considering the high number of tools available and the high interest of the practitioners to be supported by tools to automate the steps of Machine Learning pipelines, little is known concerning MLOps tools and their functionalities. To this aim, we conducted a Multivocal Literature Review (MLR) to (i) extract tools that allow for and support the creation of MLOps pipelines and (ii) analyze their main characteristics and features to provide a comprehensive overview of their value. Overall, we investigate the functionalities of 13 MLOps Tools. Our results show that most MLOps tools support the same features but apply different approaches that can bring different advantages, depending on user requirements.

A Preliminary Conceptualization and Analysis on Automated Static Analysis Tools for Vulnerability Detection in Android Apps.*

Abstract. The availability of dependable mobile apps is a crucial need for over three billion people who use apps daily for any social and emergency connectivity. A key challenge for mobile developers concerns the detection of security-related issues. While a number of tools have been proposed over the years—especially for the Android operating system — we point out a lack of empirical investigations on the actual support provided by these tools; these might guide developers in selecting the most appropriate instruments to improve their apps. In this paper, we propose a preliminary conceptualization of the vulnerabilities detected by three automated static analysis tools such as AndroBugs2, Trueseeing, and Insider. We first derive a taxonomy of the issues detectable by the tools. Then, we run the tools against a dataset composed of 6,500 Android apps to investigate their detection capabilities in terms of frequency of detection of vulnerabilities and complementarity among tools. Key findings of the study show that current tools identify similar concerns, but they use different naming conventions. Perhaps more importantly, the tools only partially cover the most common vulnerabilities classified by the Open Web Application Security Project (OWASP) Foundation.

AI-based Emotion Recognition to Study Users' Perception of Dark Patterns.*

Abstract. Dark Patterns are design patterns used to trick users into acting against their real interest. The web provides an infinite number of services accessible to anyone, which do not always promote a good user experience and are often structured with the aim of leading the user to perform unwanted actions or discourage him from making decisions that could damage the company. This is a very common practice, especially in neuromarketing. Human behavioral and perceptual patterns are cleverly exploited to achieve a specific goal. For this reason, dark pattern developers try to create an environment that invites as much purchase as possible by stimulating the customer's unconscious. Among the areas in which these strategies are adopted is tourism: online travel agency websites promote "fake discounts" for the products/services they are selling, display inaccurate pricing information leading to incorrect pricing assumptions, thus misleading consumers. One of the goals of this work is to identify which dark patterns are most used in online travel agencies and once identified, they will be used to run scenarios that will simulate booking a vacation online. During the execution of the tests, users will be filmed via webcam track- ing their expressions and emotions through AI-based facial recognition. Finally, the data obtained from the tests will be analyzed to study the emotions and feel- ings that a user feels when he/she is confronted with dark patterns, to under- stand which users are more at risk and which are the types of dark patterns to which they are more vulnerable.

A Bi-level Evolutionary Approach for the Multi-label Detection of Smelly Classes.*

Abstract. This paper presents a new evolutionary method and tool called BMLDS (Bi-level Multi-Label Detection of Smells) that optimizes a population of classifier chains for the multi-label detection of smells. As the chain is sensitive to the labels' (i.e., smell types) order, the chains induction task is framed as a bi-level optimization problem, where the upper-level role is to search for the optimal order of each considered chain while the lower-level one is to generate the chains. This allows taking into consideration the interactions between smells in the multi-label detection process. The statistical analysis of the experimental results reveals the merits of our proposal with respect to several existing works.

A Preliminary Study on the Assignment of GitHub Issues to Issue Commenters and the Relationship with Social Smells.*

Abstract. GitHub is the world's largest software hosting platform. Its features affect millions of developers. Investigating the impact of GitHub features on software teams is essential to gain insights into features' usefulness. As a preliminary step in this direction, this paper explores the relationship between the use of one GitHub feature and the social structure of the projects that adopt the feature. We explore whether the feature is used and whether the feature is associated with positive or negative changes in the team’s social structure. In this paper, we report on a preliminary study of 13 projects that used the GitHub "assign issues to issue commenters" feature. We examine the social smells in the software teams before and after the introduction of this new feature using statistical and temporal analysis. Our results indicate that the usage of this feature varied across the analyzed projects. We also find that social smells that reflect low or missing communications (Organizational Silo and Missing Links) decrease in most of the projects that used the feature consistently. The results suggest that the social structure of the teams has a positive relationship with the feature adoption. Still, future research should study the feature’s impact (and its use cases) on other aspects and over longer time periods to learn its diverse and long-term benefits on the social structure of software projects.

Regularity or Anomaly? On The Use of Anomaly Detection for Fine-Grained Just-in-Time Defect Prediction.*

Abstract. Fine-grained just-in-time defect prediction aims at identifying likely defective files within new commits pushed by developers onto a shared repository. Most of the techniques proposed in literature are based on supervised learning, where machine learning algorithms are fed with historical data. One of the limitations of these techniques is concerned with the use of imbalanced data that only contain a few defective samples to enable a proper learning phase. To overcome this problem, recent work has shown that anomaly detection methods can be used as an alternative to supervised learning, given that these do not necessarily need labelled samples. We aim at assessing how anomaly detection methods can be employed for the problem of fine-grained just-in-time defect prediction. We conduct an empirical investigation on 32 open-source projects, designing and evaluating three anomaly detection methods for fine-grained just-in-time defect prediction. However, our results are negative because anomaly detection methods, taken alone, do not overcome the prediction performance of existing machine learning solutions.

Good Fences Make Good Neighbours? On the Impact of Cultural and Geographical Dispersion on Community Smells.*

Abstract. Software development is de facto a social activity that often involves people from all places to join forces globally. In such common instances, project managers must face social challenges, e.g., personality conflicts and language barriers, which often amount literally to "culture shock". In this paper, we seek to analyze and illustrate how cultural and geographical dispersion—that is, how much a community is diverse in terms of its members' cultural attitudes and geographical collocation—influence the emergence of collaboration and communication problems in open-source communities, a.k.a. community smells, the socio-technical precursors of unforeseen, often nasty organizational conditions amounting collectively to the phenomenon called social debt. We perform an extensive empirical study on cultural characteristics of GitHub developers, and build a regression model relating the two types of dispersion—cultural and geographical—with the emergence of four types of commu- nity smells, i.e., Organizational Silo, Lone Wolf, Radio Silence, and Black Cloud. Results indicate that cultural and geographical factors influence collaboration and communication within open-source communities, to an extent which incites—or even more interest- ingly mitigates, in some cases—community smells, e.g., Lone Wolf, in development teams. Managers can use these findings to address their own organizational structure and tentatively diagnose any nasty phenomena related to the conditions under study.

Toward Understanding the Impact of Refactoring on Program Comprehension.*

Abstract. Software refactoring is the activity associated with developers changing the internal structure of source code without modifying its external behavior. The literature argues that refactoring might have beneficial and harmful implications for software maintainability, primarily when performed without the support of automated tools. This paper continues the narrative on the effects of refactoring by exploring the dimension of program comprehension, namely the property that describes how easy it is for developers to understand source code. We start our investigation by assessing the basic unit of program comprehension, namely program readability. Next, we set up a large-scale empirical investigation – conducted on 156 open-source projects – to quantify the impact of refactoring on program readability. First, we mine refactoring data and, for each commit involving a refactoring, we compute (i) the amount and type(s) of refactoring actions performed and (ii) eight state-of-the-art program comprehension metrics. Afterwards, we build statistical models relating the various refactoring operations to each of the readability metrics considered to quantify the extent to which each refactoring impacts the metrics in either a positive or negative manner. The key results are that refactoring has a notable impact on most of the readability metrics considered.

On the Evolution of Inheritance and Delegation Mechanisms and Their Impact on Code Quality.*

Abstract. Source code reuse is considered one of the holy grails of modern software development. Indeed, it has been widely demonstrated that this activity decreases software development and maintenance costs while increasing its overall trustworthiness. The Object-Oriented (OO) paradigm provides different internal mechanisms to favor code reuse, i.e., specification inheritance, implementation inheritance, and delegation. While previous studies investigated how inheritance relations impact source code quality, there is still a lack of understanding of their evolutionary aspects and, more particular, of how these mechanisms may impact source code quality over time. To bridge this gap of knowledge, this paper proposes an empirical investigation into the evolution of specification inheritance, implementation inheritance, and delegation and their impact on the variability of source code quality attributes. First, we assess how the implementation of those mechanisms varies over 15 releases of three software systems. Second, we devise a statistical approach with the aim of understanding how inheritance and delegation let source code quality—as indicated by the severity of code smells—vary in either positive or negative manner. The key results of the study indicate that inheritance and delegation evolve over time, but not in a statistically significant manner. At the same time, their evolution often leads code smell severity to be reduced, hence possibly contributing to improve code maintainability.

Gender Diversity and Community Smells: A Double-Replication Study on Brazilian Software Teams.*

Abstract. Social debts in software teams are gaining increasing attention from the research community due to their potential adverse effects on software quality. For instance, community smells are indicators of sub-optimal organizational structures and may well lead to the emergence of social debt. Previous studies analyzed which factors influence the emergence/mitigation of such smells. In particular, studies by Catolino et al. showed how factors related to team composition, particularly gender diversity, correlated to the mitigation of community smells. However, a confirmation survey on 60 practitioners suggested that these results were not aligned with the experts’ perceptions. In addition, in a separate survey, Catolino et al. collected the most common team refactoring strategies for those community smells. In this work we replicate two studies by those authors, focusing on the Brazilian software teams; culture-specific expectations on the behavior of people of different genders might have affected the perception of the importance of gender diversity and refactoring strategies when mitigating community smells. We translated the survey instrument used by Catolino et al. to Brazilian Portuguese and recruited 184 Brazilian developers. Results did not show significant differences from the original study; indeed, participants perceived gender diversity as less valuable to mitigate community smells than such factors like experience or team size. Additionally, we performed a qualitative analysis of an open question within the questionnaire for the refactoring strategies. Brazilian developers agree with the original studies for most smells, mainly promoting restructuring communities, creating a communication plan and mentoring. We believe these results provide further evidence on the problem and its implications when managing software teams, avoiding technical debt and maintenance issues due to team communication and coordination problems.

A Possibilistic Evolutionary Approach to Handle the Uncertainty of Software Metrics Thresholds in Code Smells Detection.*

Abstract. A code smells detection rule is a combination of metrics with their corresponding crisp thresholds and labels. The goal of this paper is to deal with metrics' thresholds uncertainty; as usual, such thresholds could not be exactly determined to judge the smelliness of a particular software class. To deal with this issue, we first propose to encode each metric value into a binary possibility distribution with respect to a threshold computed from a discretization technique; using the Possibilistic C-means classifier. Then, we propose ADIPOK-UMT as an evolutionary algorithm that evolves a population of PK-NN classifiers for the detection of smells under thresholds' uncertainty. The experimental results reveal that the possibility distribution-based encoding allows the implicit weighting of software metrics (features) with respect to their computed discretization thresholds. Moreover, ADIPOK-UMT is shown to outperform four relevant state-of-art approaches on a set of commonly adopted benchmark software systems.

Understanding Community Smells Variability: A Statistical Approach.*

Abstract. Social debt has been defined as the presence in a project of costly sub-optimal organizational conditions, e.g., non-cohesive development communities whose members have communication or coordination issues. Community smells are indicators of such sub-optimal organizational structures and may well lead to social debt. Recently, several studies analyzed actors affecting presence of community smells and their harmfulness, or proposed refactoring strategies to mitigate them. However, to the best of our knowledge, there is still a limited understanding of the factors influencing the variability of community smells, namely how they increase/decrease in magnitude over time. In this paper, we aim at conducting the first statistical experimentation on the matter, by analyzing how a set of 40 socio-technical factors, e.g., turnover or communicability, impact the variability of four community smells on a dataset composed of 60 open-source communities. The results of the study reveal that communicability is, in most cases, important to reduce the risk of an increase of community smell instances, while broadening the collaboration network does not always have a positive effect.

Just-In-Time Test Smell Detection and Refactoring: The DARTS Project.*

Abstract. Test smells represent sub-optimal design or implementation solutions applied when developing test cases. Previous research has shown that these smells may decrease both maintainability and effectiveness of tests and, as such, researchers have been devising methods to automatically detect them. Nevertheless, there is still a lack of tools that developers can use within their integrated devel- opment environment to identify test smells and refactor them. In this paper, we present DARTS (Detection And Refactoring of Test Smells), an Intellij plug-in which (1) implements a state-of-the-art detection mechanism to detect instances of three test smell types, i.e., General Fixture, Eager Test, and Lack of Cohesion of Test Meth- ods, at commit-level and (2) enables their automated refactoring through the integrated APIs provided by Intellij.

Refactoring Android-specific Energy Smells: A Plugin for Android Studio.*

Abstract. Mobile applications are major means to perform daily actions, including social and emergency connectivity. However, their usability is threatened by energy consumption that may be impacted by code smells, i.e., symptoms of bad implementation and design practices. In particular, researchers derived a set of mobile-specific code smells resulting in increased energy consumption of mobile apps and removing such smells through refactoring can mitigate the problem. In this paper, we extend and revise aDoctor, a tool that we previously implemented to identify energy-related smells. On the one hand, we present and implement automated refactoring solutions to those smells. On the other hand, we make the tool completely open-source and available in Android Studio as a plugin pub- lished in the official store. The video showing the tool in action is available at: https://www.youtube.com/watch?v=1c2EhVXiKis

OpenSZZ: A Free, Open-Source, Web-Accessible Implementation of the SZZ Algorithm.*

Abstract. The accurate identification of defect-inducing commits represents a key problem for researchers interested in studying the naturalness of defects and defining defect prediction models. To tackle this problem, software engineering researchers have relied on and proposed several implementations of the well-known Sliwerski-Zimmermann-Zeller (SZZ) algorithm. Despite its popularity and wide usage, no open-source, publicly available, and web-accessible implementation of the algorithm has been proposed so far. In this paper, we prototype and make available one such implementation for further use by practitioners and researchers alike. The evaluation of the proposed prototype showed competitive results and lays the foundation for future work. This paper outlines our prototype, illustrating its usage and reporting on its evaluation in action.

Developer-Driven Code Smell Prioritization.*

Abstract. Code smells are symptoms of poor implementation choices applied during software evolution. While previous research has devoted effort in the definition of automated solutions to detect them, still little is known on how to support developers when prioritizing them. Some works attempted to deliver solutions that can rank smell instances based on their severity, computed on the basis of software metrics. However, this may not be enough since it has been shown that the recommendations provided by current approaches do not take the developer's perception of design issues into account. In this paper, we perform a first step toward the concept of developer-driven code smell prioritization and propose an approach based on machine learning able to rank code smells according to the perceived criticality that developers assign to them. We evaluate our technique in an empirical study to investigate its accuracy and the features that are more relevant for classifying the developer's perception. Finally, we compare our approach with a state-of-the-art technique. Key findings show that the our solution has an F-Measure up to 85% and outperforms the baseline approach.

Testing of Mobile Applications in the Wild: A Large-Scale Empirical Study on Android Apps.*

Abstract. Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today's world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical studies aiming at assessing the actual quality of test cases developed by mobile developers: this perspective could provide evidence-based findings on the current status of testing in the wild as well as on the future research directions in the field. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.

Refactoring Community Smells in the Wild: The Practitioner's Field Manual.*

Abstract. Community smells have been defined as sub-optimal organizational structures that may lead to social debt. Previous studies have shown that they are highly diffused in both open- and closed-source projects, are perceived as harmful by practitioners, and can even lead to the introduction of technical debt in source code. Despite the presence of this body of research, little is known on the practitioners’ perceived prominence of community smells in practice as well as on the strategies adopted to deal with them. This paper aims at bridging this gap by proposing an empirical study in which 76 software practitioners are inquired on (i) the prominence of four well-known community smells, i.e., Organizational Silo, Black Cloud, Lone Wolf, and Radio Silence, in their contexts and (ii) the methods they adopted to "refactor" them. Our results first reveal that community smells frequently manifest themselves in software projects and, more importantly, there exist specific refactoring practices to deal with each of the considered community smells.

UI Dark Patterns and Where to Find Them: A Study on Mobile Applications and User Perception.*

Abstract. A Dark Pattern (DP) is an interface maliciously crafted to deceive users into performing actions they did not mean to do. Although design experts have reported on DPs extensively, little effort has been made to study how pervasive they are, especially in mobile applications. In this work, we analyze DPs in 240 popular apps and conduct an online study with 589 users on how they perceive DPs. The results of the analysis showed that 95% of apps contain one or more forms of DPs and, on average, popular applications include at least seven different types of deceiving UIs. The online study shows that most users do not recognize DPs, and they would change their behavior on app usage once informed about them. We discuss the impact of our work and what measures could be applied to alleviate malicious design issues.

On the Distribution of Test Smells in Open Source Android Applications: An Exploratory Study.*

Abstract. The impact of bad programming practices, such as code smells, in production code has been the focus of numerous studies in software engineering. Like production code, unit tests are also affected by bad programming practices which can have a negative impact on the quality and maintenance of a software system. While several studies addressed code and test smells in desktop applications, there is little knowledge of test smells in the context of mobile applications. In this study, we extend the existing catalog of test smells by identifying and defining new smells and survey over 40 developers who confirm that our proposed smells are bad programming practices in test suites. Additionally, we perform an empirical study on the occurrences and distribution of the proposed smells on 656 open-source Android apps. Our findings show a widespread occurrence of test smells in apps. We also show that apps tend to exhibit test smells early in their lifetime with different degrees of co-occurrences on different smell types. This empirical study demonstrates that test smells can be used as an indicator for necessary preventive software maintenance for test suites.

How the Experience of Development Teams Relates to Assertion Density of Test Classes.*

Abstract. The impact of developers’ experience on several development practices has been widely investigated in the past. One of the most promising research fields is software testing, as many researchers found significant correlations between developers’ experience and testing effectiveness. In this paper, we aim at further studying this relation, by focusing on how development teams’ experience is associated with the assertion density, i.e., the number of assertions per test class KLOC, that has previously been shown as an effective way to decrease fault density. We perform a mixed-methods empirical study. First, we devise a statistical model relating development teams’ experience and other control factors to the assertion density of test classes belonging to 12 software projects. This model enables us to investigate whether experience comes out as a statistically significant factor to explain assertion density. Second, we contrast the statistical findings with a survey study conducted with 57 developers, who were asked their opinions on how developer’s experience is related to the way they add assertions in test code. Our findings suggest the existence of a relationship: on the one hand, the development team’s experience is a statistically significant factor in most of the systems that we have investigated; on the other hand, developers confirm the importance of experience and team composition for the effective testing of production code.

Adoption, Support, and Challenges of Infrastructure-as-Code: Insights from Industry.*

Abstract. Infrastructure-as-code (IaC) is the DevOps tactic of managing and provisioning infrastructure through machinereadable definition files, rather than physical hardware configuration or interactive configuration tools. From a maintenance and evolution perspective, the topic has piqued the interest of practitioners and academics alike, given the relative scarcity of supporting patterns, best practices, tools, and software engineering techniques. Using the data coming from 44 semi-structured interviews in as many companies, in this paper we shed light on the state of the practice in the adoption of IaC and the key software engineering challenges in the field. Particularly, we investigate (i) how practitioners adopt and develop IaC, (ii) which support is currently available, i.e., the typically used tools and their advantages/disadvantages, and (iii) what are the practitioner’s needs when dealing with IaC development, maintenance, and evolution. Our findings clearly highlight the need for more research in the field: the support provided by currently available tools is still limited, and developers feel the need of novel techniques for testing and maintaining IaC code.

Understanding Flaky Tests: The Developer's Perspective.*

Abstract. Flaky tests are software tests that exhibit a seemingly random outcome (pass or fail) when run against the same, identical code. Previous work has examined fixes to flaky tests and has proposed automated solutions to locate as well as fix flaky tests—we complement it by examining the perceptions of software developers about the nature, relevance, and challenges of this phenomenon. We asked 21 professional developers to classify 200 flaky tests they previously fixed, in terms of the nature of the flakiness, the origin of the flakiness, and the fixing effort. We complement this analysis with information about the fixing strategy. Subsequently, we conducted an online survey with 121 developers with a median industrial programming experience of five years. Our research shows that: The flakiness is due to several different causes, four of which have never been reported before, despite being the most costly to fix; flakiness is perceived as significant by the vast majority of developers, regardless of their team’s size and project’s domain, and it can have effects on resource allocation, scheduling, and the perceived reliability of the test suite; and the challenges developers report to face regard mostly the reproduction of the flaky behavior and the identification of the cause for the flakiness.

@article{eck2019understanding,
  title={Understanding Flaky Tests: The Developer’s Perspective},
  author={Eck, Moritz and Palomba, Fabio and Castelluccio, Marco and Bacchelli, Alberto},
  year={2019}
}

On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later.*

Abstract. Good unit tests play a paramount role when it comes to foster and evaluate software quality. However, writing effective tests is an extremely costly and time consuming practice. To reduce such a burden for developers, researchers devised ingenious techniques to automatically generate test suite for existing code bases. Nevertheless, how automatically generated test cases fare against manually written ones is an open research question. In 2008, Bacchelli et al. conducted an initial case study comparing automatic and manually generated test suites. Since in the last ten years we have witnessed a huge amount of work on novel approaches and tools for automatic test generation, in this paper we revise their study using current tools as well as complementing their research method by evaluating these tools’ ability in finding regressions.

@inproceedings{serra2019effectiveness,
  title={On the effectiveness of manual and automatic unit test generation: ten years later},
  author={Serra, Domenico and Grano, Giovanni and Palomba, Fabio and Ferrucci, Filomena and Gall, Harald C and Bacchelli, Alberto},
  booktitle={Proceedings of the 16th International Conference on Mining Software Repositories},
  pages={121--125},
  year={2019},
  organization={IEEE Press}
}

Comparing Machine Learning and Heuristic Approaches for Metric-Based Code Smell Detection.*

Abstract. Code smells represent poor implementation choices performed by developers when enhancing source code. Their negative impact on source code maintainability and comprehensibility has been widely shown in the past and several techniques to automatically detect them have been devised. Most of these techniques are based on heuristics, namely they compute a set of code metrics and combine them by creating detection rules; while they have a reasonable accuracy, a recent trend is represented by the use of machine learning where code metrics are used as predictors of the smelliness of code artefacts. Despite the recent advances in the field, there is still a noticeable lack of knowledge of whether machine learning can actually be more accurate than traditional heuristic-based approaches. To fill this gap, in this paper we propose a large-scale study to empirically compare the performance of heuristic-based and machine-learning-based techniques for metric-based code smell detection. We consider five code smell types and compare machine learning models with DECOR, a state-of-the-art heuristic-based approach. Key findings emphasize the need of further research aimed at improving the effectiveness of both machine learning and heuristic approaches for code smell detection: while DECOR generally achieves better performance than a machine learning baseline, its precision is still too low to make it usable in practice.

Gender Diversity and Women in Software Teams: How Do They Affect Community Smells?*

Abstract. As social as software engineers are, there is a known and established gender imbalance in our community structures, regardless of their open- or closed-source nature. To shed light on the actual benefits of achieving such balance, this empirical study looks into the relations between such balance and the occurrence of community smells, that is, sub-optimal circumstances and patterns across the software organizational structure. Example of community smells are Organizational Silo effects (overly disconnected sub-groups) or Lone Wolves (defiant community members). Results indicate that the presence of women generally reduces the amount of community smells. We conclude that women are instrumental to reducing community smells in software development teams.

@inproceedings{catolino2019gender,
  title={Gender diversity and women in software teams: How do they affect community smells?},
  author={Catolino, Gemma and Palomba, Fabio and Tamburri, Damian A and Serebrenik, Alexander and Ferrucci, Filomena},
  booktitle={Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Society},
  pages={11--20},
  year={2019},
  organization={IEEE Press}
}

Test-Driven Code Review: An Empirical Study.*

Abstract. Test-Driven Code Review (TDR) is a code review practice in which a reviewer inspects a patch by examining the changed test code before the changed production code. Although this practice has been mentioned positively by practitioners in informal literature and interviews, there is no systematic knowledge on its effects, prevalence, problems, and advantages. In this paper, we aim at empirically understanding whether this practice has an effect on code review effectiveness and how developers’ perceive TDR. We conduct (i) a controlled experiment with 93 developers that perform more than 150 reviews, and (ii) 9 semi-structured interviews and a survey with 103 respondents to gather information on how TDR is perceived. Key results from the experiment show that developers adopting TDR find the same proportion of defects in production code, but more in test code, at the expenses of less maintainability issues in production code. Furthermore, we found that most developers prefer to review production code as they deem it more important and tests should follow from it. Moreover, widespread poor test code quality and no tool support hinder the adoption of TDR.

Information Needs in Contemporary Code Review.*

Abstract. Contemporary code review is a widespread practice used by software engineers to maintain high software quality and share project knowledge. However, conducting proper code review takes time and developers often have limited time for review. In this paper, we aim at investigating the information that reviewers need to conduct a proper code review, to better understand this process and how research and tool support can make developers become more effective and efficient reviewers. Previous work has provided evidence that a successful code review process is one in which reviewers and authors actively participate and collaborate. In these cases, the threads of discussions that are saved by code review tools are a precious source of information that can be later exploited for research and practice. In this paper, we focus on this source of information as a way to gather reliable data on the aforementioned reviewers’ needs. We manually analyze 900 code review comments from three large open-source projects and organize them in categories by means of a card sort. Our results highlight the presence of seven high-level information needs, such as knowing the uses of methods and variables declared/modified in the code under review. Based on these results we suggest ways in which future code review tools can better support collaboration and the reviewing task.

@article{pascarella2018information,
  title={Information needs in contemporary code review},
  author={Pascarella, Luca and Spadini, Davide and Palomba, Fabio and Bruntink, Magiel and Bacchelli, Alberto},
  journal={Proceedings of the ACM on Human-Computer Interaction},
  volume={2},
  number={CSCW},
  pages={135},
  year={2018},
  publisher={ACM}
}

Mining File Histories: Should We Consider Branches?*

Abstract. Modern distributed version control systems, such as Git, offer support for branching — the possibility to develop parts of software outside the master trunk. Consideration of the repository structure in Mining Software Repository (MSR) studies requires a thorough approach to mining, but there is no well-documented, widespread methodology regarding the handling of merge commits and branches. Moreover, there is still a lack of knowledge of the extent to which considering branches during MSR studies impacts the results of the studies. In this study, we set out to evaluate the importance of proper handling of branches when calculating file modification histories. We analyze over 1,400 Git repositories of four open source ecosystems and compute modification histories for over two million files, using two different algorithms. One algorithm only follows the first parent of each commit when traversing the repository, the other returns the full modification history of a file across all branches. We show that the two algorithms consistently deliver different results, but the scale of the difference varies across projects and ecosystems. Further, we evaluate the importance of accurate mining of file histories by comparing the performance of common techniques that rely on file modification history — reviewer recommendation, change recommendation, and defect prediction — for two algorithms of file history retrieval. We find that considering full file histories leads to an increase in the techniques’ performance that is rather modest.

@inproceedings{kovalenko2018mining,
  title={Mining file histories: should we consider branches?},
  author={Kovalenko, Vladimir and Palomba, Fabio and Bacchelli, Alberto},
  booktitle={Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering},
  pages={202--213},
  year={2018},
  organization={ACM}
}

Continuous Code Quality: Are We (Really) Doing That?*

Abstract. Continuous Integration (CI) is a software engineering practice where developers constantly integrate their changes to a project through an automated build process. The goal of CI is to provide developers with prompt feedback on several quality dimensions after each change. Indeed, previous studies provided empirical evidence on a positive association between properly following CI principles and source code quality. A core principle behind CI is Continuous Code Quality (also known as CCQ, which includes automated testing and automated code inspection) may appear simple and effective, yet we know little about its practical adoption. In this paper, we propose a preliminary empirical investigation aimed at understanding how rigorously practitioners follow CCQ. Our study reveals a strong dichotomy between theory and practice: developers do not perform continuous inspection but rather control for quality only at the end of a sprint and most of the times only on the release branch.

@inproceedings{vassallo2018continuous,
  title={Continuous code quality: are we (really) doing that?},
  author={Vassallo, Carmine and Palomba, Fabio and Bacchelli, Alberto and Gall, Harald C},
  booktitle={Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering},
  pages={790--795},
  year={2018},
  organization={ACM}
}

Continuous Refactoring in CI: A Preliminary Study On the Perceived Advantages and Barriers*

Abstract. By definition, the practice of Continuous Integration (CI) promotes continuous software quality improvement. In systems adopting such a practice, quality assurance is usually performed by using static and dynamic analysis tools (e.g., SonarQube) that compute overall metrics such as maintainability or reliability measures. Furthermore, developers usually define quality gates, i.e., source code quality thresholds that must be reached by the software product after every newly committed change. If a quality gate fails (e.g., a maintainability metric is below a certain threshold), developers should refactor the code possibly addressing some of the proposed warnings. While previous research findings showed that refactoring is often not done in practice, it is still unclear whether and how the adoption of a CI philosophy has changed the way developers perceive and adopt refactoring. In this paper, we preliminarily study—running a survey study that involves 31 developers—how developers perform refactoring in CI, which needs they have and the barriers they face while continuously refactor source code.

@inproceedings{vassallo2018continuous,
  title={Continuous refactoring in ci: A preliminary study on the perceived advantages and barriers},
  author={Vassallo, Carmine and Palomba, Fabio and Gall, Harald C},
  booktitle={2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  pages={564--568},
  year={2018},
  organization={IEEE}
}

On The Relation of Test Smells to Software Code Quality*

Abstract. Test smells are sub-optimal design choices in the implementation of test code. As reported by recent studies, their presence might not only negatively affect the comprehension of test suites, but can also lead to test cases being less effective in finding bugs in production code. Although important steps toward understanding test smells, there is still a notable absence of studies assessing their association with software quality. In this paper, we investigate the relationship between the presence of test smells and the change- and defect-proneness of test code, as well as the defect-proneness of the production code being tested. To this aim, we collect data pertaining to 221 releases of ten software systems and we analyze more than a million test cases to investigate the association of six test smells and their co-occurrence with software quality. Key results of our study include: (i) tests with smells are more change- and defect-prone, (ii) ‘Indirect Testing’, ‘Eager Test’, and ‘Assertion Roulette’ are the most significant smells for change-proneness and, (iii) production code is more defect-prone when tested by smelly tests.

@inproceedings{spadini2018relation,
  title={On the relation of test smells to software code quality},
  author={Spadini, Davide and Palomba, Fabio and Zaidman, Andy and Bruntink, Magiel and Bacchelli, Alberto},
  booktitle={2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  pages={1--12},
  year={2018},
  organization={IEEE}
}

Automatic Test Smell Detection Using Information Retrieval Techniques*

Abstract. Software testing is a key activity to control the reliability of production code. Unfortunately, the effectiveness of test cases can be threatened by the presence of faults. Recent work showed that static indicators can be exploited to identify testrelated issues. In particular test smells, i.e., sub-optimal design choices applied by developers when implementing test cases, have been shown to be related to test case effectiveness. While some approaches for the automatic detection of test smells have been proposed so far, they generally suffer of poor performance: as a consequence, current detectors cannot properly provide support to developers when diagnosing the quality of test cases. In this paper, we aim at making a step ahead toward the automated detection of test smells by devising a novel textual-based detector, coined TASTE (Textual AnalySis for Test smEll detection), with the aim of evaluating the usefulness of textual analysis for detecting three test smell types, General Fixture, Eager Test, and Lack of Cohesion of Methods. We evaluate TASTE in an empirical study that involves a manually-built dataset composed of 494 test smell instances belonging to 12 software projects, comparing the capabilities of our detector with those of two code metrics-based techniques proposed by Van Rompaey et al. and Greiler et al. Our results show that the structural-based detection applied by existing approaches cannot identify most of the test smells in our dataset, while TASTE is up to 44% more effective. Finally, we find that textual and structural approaches can identify different sets of test smells, thereby indicating complementarity.

@inproceedings{palomba2018automatic,
  title={Automatic test smell detection using information retrieval techniques},
  author={Palomba, Fabio and Zaidman, Andy and De Lucia, Andrea},
  booktitle={2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  pages={311--322},
  year={2018},
  organization={IEEE}
}

BECLoMA: Augmenting Stack Traces with User Review Information.*

Abstract. Mobile devices such as smartphones, tablets and wearables are changing the way we do things, radically modifying our approach to technology. To sustain the high competition characterizing the mobile market, developers need to deliver high quality applications in a short release cycle. To reveal and fix bugs as soon as possible, researchers and practitioners proposed tools to automate the testing process. However, such tools generate a high number of redundant inputs, lacking of contextual information and generating reports difficult to analyze. In this context, the content of user reviews represents an unmatched source for developers seeking for defects in their applications. However, no prior work explored the adoption of information available in user reviews for testing purposes. In this demo we present BECLOMA, a tool to enable the integration of user feedback in the testing process of mobile apps. BECLOMA links information from testing tools and user reviews, presenting to developers an augmented testing report combining stack traces with user reviews information referring to the same crash. We show that BECLOMA facilitates not only the diagnosis and fix of app bugs, but also presents additional benefits: it eases the usage of testing tools and automates the analysis of user reviews from the Google Play Store.

@inproceedings{pelloni2018becloma,
  title={Becloma: Augmenting stack traces with user review information},
  author={Pelloni, Lucas and Grano, Giovanni and Ciurumelea, Adelina and Panichella, Sebastiano and Palomba, Fabio and Gall, Harald C},
  booktitle={2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={522--526},
  year={2018},
  organization={IEEE}
}

Do Developers Update Third-Party Libraries in Mobile Apps?*

Abstract. One of the most common strategies to develop new software is to take advantage of existing source code, which is available in comprehensive packages called third-party libraries. As for all software systems, even these libraries change to offer new functionalities and fix bugs or security issues. The way the changes are propagated has been studied by researchers, interested in understanding their impact on the non-functional attributes of the systems source code. While the research community mainly focused on the change propagation phenomenon in the context of traditional applications, only little is known regarding the mobile context. In this paper, we aim at bridging this gap by conducting an empirical study on the evolution history of 291 mobile apps, by investigating (i) whether mobile developers actually update third-party libraries, (ii) which are the categories of libraries with respect to the developers’ proneness to update their apps, (iii) what are the common patterns followed by developers when updating a software library, and (iv) whether high- and low-rated apps present peculiar update patterns. The results of the study showed that mobile developers rarely update their apps with respect to the used libraries, and when they do, they mainly tend to update the libraries related to the Graphical User Interface, with the aim of keeping the mobile apps updated with the latest design tendencies. In some cases developers ignore updates because of a poor awareness of the benefits, or a too high cost/benefit ratio. Finally, high- and low-rated apps present strong differences.

@inproceedings{salza2018developers,
  title={Do developers update third-party libraries in mobile apps?},
  author={Salza, Pasquale and Palomba, Fabio and Di Nucci, Dario and D'Uva, Cosmo and De Lucia, Andrea and Ferrucci, Filomena},
  booktitle={Proceedings of the 26th Conference on Program Comprehension},
  pages={255--265},
  year={2018},
  organization={ACM}
}

How Is Video Game Development Different from Software Development in Open Source?*

Abstract. Recent research has provided evidence that, in the industrial context, developing video games diverges from developing software systems in other domains, such as office suites and system utilities. In this paper, we consider video game development in the open source system (OSS) context. Specifically, we investigate how developers contribute to video games vs. non-games by working on different kinds of artifacts, how they handle malfunctions, and how they perceive the development process of their projects. To this purpose, we conducted a mixed, qualitative and quantitative study on a broad suite of 60 OSS projects. Our results confirm the existence of significant differences between game and non-game development, in terms of how project resources are organized and in the diversity of developers’ specializations. Moreover, game developers responding to our survey perceive more difficulties than other developers when reusing code as well as performing automated testing, and they lack a clear overview of their system’s requirements.

@inproceedings{pascarella2018video,
  title={How is video game development different from software development in open source?},
  author={Pascarella, Luca and Palomba, Fabio and Di Penta, Massimiliano and Bacchelli, Alberto},
  booktitle={2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR)},
  pages={392--402},
  year={2018},
  organization={IEEE}
}

A Graph-based Dataset of Commit History of Real-World Android apps.*

Abstract. Empirical studies on the engineering of Android apps need to be based on open datasets and tools to allow comparisons, improve generalizability, and enable replicability. However, obtaining a good dataset is problematic and this state of things slows down empirical research on this topic. In this paper, we contribute to overcome this challenge by presenting the first, self-contained, publicly available dataset weaving spread-out data sources about real-world, open-source Android apps. Our dataset is encoded as a graph-based database and contains the following information about 8,431 real open-source Android apps: (i) metadata about their GitHub projects, (ii) Git repositories with full commit history and (iii) metadata extracted from the Google Play store, such as app ratings and permissions. The dataset is available in Docker images to ease adoption.

@inproceedings{geiger2018graph,
  title={A graph-based dataset of commit history of real-world android apps},
  author={Geiger, Franz-Xaver and Malavolta, Ivano and Pascarella, Luca and Palomba, Fabio and Di Nucci, Dario and Bacchelli, Alberto},
  booktitle={Proceedings of the 15th International Conference on Mining Software Repositories},
  pages={30--33},
  year={2018},
  organization={ACM}
}

Self-Reported Activities of Android Developers.*

Abstract. To gain a deeper empirical understanding of how developers work on Android apps, we investigate self-reported activities of Android developers and to what extent these activities can be classified with machine learning techniques. To this aim, we firstly create a taxonomy of self-reported activities coming from the manual analysis of 5,000 commit messages from 8,280 Android apps. Then, we study the frequency of each category of self-reported activities identified in the taxonomy, and investigate the feasibility of an automated classification approach. Our findings can inform be used by both practitioners and researchers to take informed decisions or support other software engineering activities.

@inproceedings{pascarella2018self,
  title={Self-reported activities of android developers},
  author={Pascarella, Luca and Geiger, Franz-Xaver and Palomba, Fabio and Di Nucci, Dario and Malavolta, Ivano and Bacchelli, Alberto},
  booktitle={2018 IEEE/ACM 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft)},
  pages={144--155},
  year={2018},
  organization={IEEE}
}

Re-evaluating Method-Level Bug Prediction.*

Abstract. Bug prediction is aimed at supporting developers in the identification of code artifacts more likely to be defective. Most approaches defined so far target the prediction of bugs at class-level, thus pinpointing the presence of a bug in an entire source file. Nevertheless, past research has provided evidence that this granularity might be too coarse-grained, thus reducing the usability of bug prediction in practice. As a consequence, researchers have started proposing method-level bug prediction models, showing promising evidence that it is possible to operate at this level of granularity. In this study, we first replicate previous research on methodlevel bug prediction on different systems/timespans. Afterwards, we reflect on the evaluation strategy and propose a more realistic one. Key results of our study show that the performance of the method-level bug prediction model is similar to what previously reported also for different systems/timespans, when evaluated with the same strategy. However—when evaluated with a more realistic strategy—all the models show a dramatic drop in performance showing results close to that of a random classifiers. Our replication and negative results indicate that method-level bug prediction is still an open challenge.

@inproceedings{pascarella2018re,
  title={Re-evaluating method-level bug prediction},
  author={Pascarella, Luca and Palomba, Fabio and Bacchelli, Alberto},
  booktitle={2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={592--601},
  year={2018},
  organization={IEEE}
}

Detecting Code Smells using Machine Learning Techniques: Are We There Yet?*

Abstract. Code smells are symptoms of poor design and implementation choices weighing heavily on the quality of produced source code. During the last decades several code smell detection tools have been proposed. However, the literature shows that the results of these tools can be subjective and are intrinsically tied to the nature and approach of the detection. In a recent work Arcelli Fontana et al. [1] proposed the use of Machine-Learning (ML) techniques for code smell detection, possibly solving the issue of tool subjectivity giving to a learner the ability to discern between smelly and non-smelly source code elements. While this work opened a new perspective for code smell detection, in the context of our research we found a number of possible limitations that might threaten the results of this study. The most important issue is related to the metric distribution of smelly instances in the used dataset, which is strongly different than the one of nonsmelly instances. In this work, we investigate this issue and our findings show that the high performance achieved in the study by Arcelli Fontana et al. was in fact due to the specific dataset employed rather than the actual capabilities of machine-learning techniques for code smell detection.

@inproceedings{di2018detecting,
  title={Detecting code smells using machine learning techniques: are we there yet?},
  author={Di Nucci, Dario and Palomba, Fabio and Tamburri, Damian A and Serebrenik, Alexander and De Lucia, Andrea},
  booktitle={2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={612--621},
  year={2018},
  organization={IEEE}
}

Context Is King: The Developer Perspective on the Usage of Static Analysis Tools.*

Abstract. Automatic static analysis tools (ASATs) are tools that support automatic code quality evaluation of software systems with the aim of (i) avoiding and/or removing bugs and (ii) spotting design issues. Hindering their wide-spread acceptance are their (i) high false positive rates and (ii) low comprehensibility of the generated warnings. Researchers and ASATs vendors have proposed solutions to prioritize such warnings with the aim of guiding developers toward the most severe ones. However, none of the proposed solutions considers the development context in which an ASAT is being used to further improve the selection of relevant warnings. To shed light on the impact of such contexts on the warnings configuration, usage and adopted prioritization strategies, we surveyed 42 developers (69% in industry and 31% in open source projects) and interviewed 11 industrial experts that integrate ASATs in their workflow. While we can confirm previous findings on the reluctance of developers to configure ASATs, our study highlights that (i) 71% of developers do pay attention to different warning categories depending on the development context, and (ii) 63% of our respondents rely on specific factors (e.g., team policies and composition) when prioritizing warnings to fix during their programming. Our results clearly indicate ways to better assist developers by improving existing warning selection and prioritization strategies.

@inproceedings{vassallo2018context,
  title={Context is king: The developer perspective on the usage of static analysis tools},
  author={Vassallo, Carmine and Panichella, Sebastiano and Palomba, Fabio and Proksch, Sebastian and Zaidman, Andy and Gall, Harald C},
  booktitle={2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={38--49},
  year={2018},
  organization={IEEE}
}

Exploring the Integration of User Feedback in Automated Testing of Android Applications.*

Abstract. The intense competition characterizing mobile application’s marketplaces forces developers to create and maintain high-quality mobile apps in order to ensure their commercial success and acquire new users. This motivated the research community to propose solutions that automate the testing process of mobile apps. However, the main problem of current testing tools is that they generate redundant and random inputs that are insufficient to properly simulate the human behavior, thus leaving feature and crash bugs undetected until they are encountered by users. To cope with this problem, we conjecture that information available in user reviews—that previous work showed as effective for maintenance and evolution problems—can be successfully exploited to identify the main issues users experience while using mobile applications, e.g., GUI problems and crashes. In this paper we provide initial insights into this direction, investigating (i) what type of user feedback can be actually exploited for testing purposes, (ii) how complementary user feedback and automated testing tools are, when detecting crash bugs or errors and (iii) whether an automated system able to monitor crashrelated information reported in user feedback is sufficiently accurate. Results of our study, involving 11,296 reviews of 8 mobile applications, show that user feedback can be exploited to provide contextual details about errors or exceptions detected by automated testing tools. Moreover, they also help detecting bugs that would remain uncovered when rely on testing tools only. Finally, the accuracy of the proposed automated monitoring system demonstrates the feasibility of our vision, i.e., integrate user feedback into testing process.

@inproceedings{grano2018exploring,
  title={Exploring the integration of user feedback in automated testing of android applications},
  author={Grano, Giovanni and Ciurumelea, Adelina and Panichella, Sebastiano and Palomba, Fabio and Gall, Harald C},
  booktitle={2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={72--83},
  year={2018},
  organization={IEEE}
}

Developer-Related Factors in Change Prediction: An Empirical Assessment.*

Abstract. Predicting the areas of the source code having a higher likelihood to change in the future is a crucial activity to allow developers to plan preventive maintenance operations such as refactoring or peer-code reviews. In the past the research community was active in devising change prediction models based on structural metrics extracted from the source code. More recently, Elish et al. showed how evolution metrics can be more efficient for predicting change-prone classes. In this paper, we aim at making a further step ahead by investigating the role of different developer-related factors, which are able to capture the complexity of the development process under different perspectives, in the context of change prediction. We also compared such models with existing change-prediction models based on evolution and code metrics. Our findings reveal the capabilities of developer-based metrics in identifying classes of a software system more likely to be changed in the future. Moreover, we observed interesting complementarities among the experimented prediction models, that may possibly lead to the definition of new combined models exploiting developer-related factors as well as product and evolution metrics.

@inproceedings{catolino2017developer,
  title={Developer-related factors in change prediction: an empirical assessment},
  author={Catolino, Gemma and Palomba, Fabio and De Lucia, Andrea and Ferrucci, Filomena and Zaidman, Andy},
  booktitle={Proceedings of the 25th International Conference on Program Comprehension},
  pages={186--195},
  year={2017},
  organization={IEEE Press}
}

An Exploratory Study on the Relationship between Changes and Refactoring.*

Abstract. Refactoring aims at improving the internal structure of a software system without changing its external behavior. Previous studies empirically assessed, on the one hand, the benefits of refactoring in terms of code quality and developers’ productivity, and on the other hand, the underlying reasons that push programmers to apply refactoring. Results achieved in the latter investigations indicate that besides personal motivation such as the responsibility concerned with code authorship, refactoring is mainly performed as a consequence of changes in the requirements rather than driven by software quality. However, these findings have been derived by surveying developers, and therefore no studies performed on the actual modifications made on software repositories have been carried out to corroborate the achieved findings. To bridge this gap, we provide a quantitative investigation on the relationship between different types of code changes (i.e., Fault Repairing Modification, Feature Introduction Modification, and General Maintenance Modification) and 28 different refactoring types coming from 3 open source projects. Results showed that developers tend to apply a higher number of refactoring operations aimed at improving maintainability and comprehensibility of the source code when fixing bugs. Instead, when new features are implemented, more complex refactoring operations are performed to improve code cohesion. Most of the times, the underlying reasons behind the application of such refactoring operations are represented by the presence of duplicate code or previously introduced self-admitted technical debts.

@inproceedings{palomba2017exploratory,
  title={An exploratory study on the relationship between changes and refactoring},
  author={Palomba, Fabio and Zaidman, Andy and Oliveto, Rocco and De Lucia, Andrea},
  booktitle={2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)},
  pages={176--185},
  year={2017},
  organization={IEEE}
}

PETrA: a Software-Based Tool for Estimating the Energy Profile of Android Applications.*

Abstract. Energy efficiency is a vital characteristic of any mobile application, and indeed is becoming an important factor for user satisfaction. For this reason, in recent years several approaches and tools for measuring the energy consumption of mobile devices have been proposed. Hardware-based solutions are highly precise, but at the same time they require costly hardware toolkits. Model-based techniques require a possibly difficult calibration of the parameters needed to correctly create a model on a specific hardware device. Finally, software-based solutions are easier to use, but they are possibly less precise than hardware-based solution. In this demo, we present PETRA, a novel software-based tool for measuring the energy consumption of Android apps. With respect to other tools, PETRA is compatible with all the smartphones with Android 5.0 or higher. We also provide evidence that our tool is able to perform similarly to hardware-based solutions.

@inproceedings{di2017petra,
  title={Petra: a software-based tool for estimating the energy profile of android applications},
  author={Di Nucci, Dario and Palomba, Fabio and Prota, Antonio and Panichella, Annibale and Zaidman, Andy and De Lucia, Andrea},
  booktitle={2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)},
  pages={3--6},
  year={2017},
  organization={IEEE}
}

Recommending and Localizing Code Changes for Mobile Apps based on User Reviews.*

Abstract. Researchers have proposed several approaches to extract information from user reviews useful for maintaining and evolving mobile apps. However, most of them just perform automatic classification of user reviews according to specific keywords (e.g., bugs, features). Moreover, they do not provide any support for linking user feedback to the source code components to be changed, thus requiring a manual, time-consuming, and error-prone task. In this paper, we introduce CHANGEADVISOR, a novel approach that analyzes the structure, semantics, and sentiments of sentences contained in user reviews to extract useful (user) feedback from maintenance perspectives and recommend to developers changes to software artifacts. It relies on natural language processing and clustering algorithms to group user reviews around similar user needs and suggestions for change. Then, it involves textual based heuristics to determine the code artifacts that need to be maintained according to the recommended software changes. The quantitative and qualitative studies carried out on 44 683 user reviews of 10 open source mobile apps and their original developers showed a high accuracy of CHANGEADVISOR in (i) clustering similar user change requests and (ii) identifying the code components impacted by the suggested changes. Moreover, the obtained results show that CHANGEADVISOR is more accurate than a baseline approach for linking user feedback clusters to the source code in terms of both precision (+47%) and recall (+38%).

@inproceedings{palomba2017recommending,
  title={Recommending and localizing change requests for mobile apps based on user reviews},
  author={Palomba, Fabio and Salza, Pasquale and Ciurumelea, Adelina and Panichella, Sebastiano and Gall, Harald and Ferrucci, Filomena and De Lucia, Andrea},
  booktitle={Proceedings of the 39th international conference on software engineering},
  pages={106--117},
  year={2017},
  organization={IEEE Press}
}

Lightweight Detection of Android-specific Code Smells: the aDoctor Project.*

Abstract. Code smells are symptoms of poor design solutions applied by programmers during the development of software systems. While the research community devoted a lot of effort to studying and devising approaches for detecting the traditional code smells defined by Fowler, little knowledge and support is available for an emerging category of Mobile app code smells. Recently, Reimann et al. proposed a new catalogue of Androidspecific code smells that may be a threat for the maintainability and the efficiency of Android applications. However, current tools working in the context of Mobile apps provide limited support and, more importantly, are not available for developers interested in monitoring the quality of their apps. To overcome these limitations, we propose a fully automated tool, coined ADOCTOR, able to identify 15 Android-specific code smells from the catalogue by Reimann et al. An empirical study conducted on the source code of 18 Android applications reveals that the proposed tool reaches, on average, 98% of precision and 98% of recall. We made ADOCTOR publicly available.

@inproceedings{palomba2017lightweight,
  title={Lightweight detection of Android-specific code smells: The aDoctor project},
  author={Palomba, Fabio and Di Nucci, Dario and Panichella, Annibale and Zaidman, Andy and De Lucia, Andrea},
  booktitle={2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={487--491},
  year={2017},
  organization={IEEE}
}

Software-based Energy Profiling of Android Apps: Simple, Efficient and Reliable?*

Abstract. Modeling the power profile of mobile applications is a crucial activity to identify the causes behind energy leaks. To this aim, researchers have proposed hardware-based tools as well as model-based and software-based techniques to approximate the actual energy profile. However, all these solutions present their own advantages and disadvantages. Hardware-based tools are highly precise, but at the same time their use is bound to the acquisition of costly hardware components. Model-based tools require the calibration of parameters needed to correctly create a model on a specific hardware device. Software-based approaches are cheaper and easier to use than hardware-based tools, but they are believed to be less precise. In this paper, we take a deeper look at the pros and cons of softwarebased solutions investigating to what extent their measurements depart from hardware-based solutions. To this aim, we propose a software-based tool named PETRA that we compare with the hardware-based MONSOON toolkit on 54 Android apps. The results show that PETRA performs similarly to MONSOON despite not using any sophisticated hardware components. In fact, the mean relative error with respect to MONSOON is always lower than 0.05. Moreover, 95% of the estimation errors are within 5% of the actual values measured using the hardware-based toolkit.

@inproceedings{di2017software,
  title={Software-based energy profiling of android apps: Simple, efficient and reliable?},
  author={Di Nucci, Dario and Palomba, Fabio and Prota, Antonio and Panichella, Annibale and Zaidman, Andy and De Lucia, Andrea},
  booktitle={2017 IEEE 24th international conference on software analysis, evolution and reengineering (SANER)},
  pages={103--114},
  year={2017},
  organization={IEEE}
}

An Empirical Investigation into the Nature of Test Smells.*

Abstract. Test smells have been defined as poorly designed tests and, as reported by recent empirical studies, their presence may negatively affect comprehension and consequently maintenance of test suites. Despite this, there are no available automated tools to support identification and removal of test smells. In this paper, we firstly investigate developers’ perception of test smells in a study with 19 developers. The results show that developers generally do not recognize (potentially harmful) test smells, highlighting that automated tools for identifying such smells are much needed. However, to build effective tools, deeper insights into the test smells phenomenon are required. To this aim, we conducted a large-scale empirical investigation aimed at analyzing (i) when test smells occur in source code, (ii) what their survivability is, and (iii) whether their presence is associated with the presence of design problems in production code (code smells). The results indicate that test smells are usually introduced when the corresponding test code is committed in the repository for the first time, and they tend to remain in a system for a long time. Moreover, we found various unexpected relationships between test and code smells. Finally, we show how the results of this study can be used to build effective automated tools for test smell detection and refactoring.

@inproceedings{tufano2016empirical,
  title={An empirical investigation into the nature of test smells},
  author={Tufano, Michele and Palomba, Fabio and Bavota, Gabriele and Di Penta, Massimiliano and Oliveto, Rocco and De Lucia, Andrea and Poshyvanyk, Denys},
  booktitle={2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE)},
  pages={4--15},
  year={2016},
  organization={IEEE}
}

Alternative Sources of Information for Code Smell Detection: Postcards from Far Away.*

Abstract. Code smells have been defined as symptoms of poor design and implementation choices. Previous studies showed the negative impact of code smells on the comprehensibility and maintainability of code. For this reasons, several detection techniques have been proposed. Most of them rely on the analysis of the properties extractable from the source code. In the context of this work, we highlight several aspects that can possibly contribute to the improvement of the current state of the art and propose our solutions, based on the analysis on how code smells are actually introduced as well as the usefulness of historical and textual information to realize more reliable code smell detectors. Finally, we present an overview of the open issues and challenges related to code smell detection and management that the research community should focus on in the next future.

@inproceedings{palomba2016alternative,
  title={Alternative sources of information for code smell detection: Postcards from far away},
  author={Palomba, Fabio},
  booktitle={2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  pages={636--640},
  year={2016},
  organization={IEEE}
}

Smells like Teen Spirit: Improving Bug Prediction Performance Using the Intensity of Code Smells.*

Abstract. Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bugproneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models and comparing the results of the new model against the baseline model. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also evaluate the actual gain provided by the intensity index with respect to the other metrics in the model, including the ones used to compute the code smell intensity. We observe that the intensity index is much more important as compared to other metrics used for predicting the buggyness of smelly classes.

@inproceedings{palomba2016smells,
  title={Smells like teen spirit: Improving bug prediction performance using the intensity of code smells},
  author={Palomba, Fabio and Zanoni, Marco and Fontana, Francesca Arcelli and De Lucia, Andrea and Oliveto, Rocco},
  booktitle={2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)},
  pages={244--255},
  year={2016},
  organization={IEEE}
}

Automatic Test Case Generation: What if Test Code Quality Matters?*