Comparing communication and development networks for predicting file change proneness: An exploratory study considering process and social metrics

Previous studies have shown that social factors of software engineering influence software quality. Communication and development networks represent the interactions among software developers. We explored the statistical relationships between file change proneness and a set metrics extracted from the issue tracker and version control system data to find the relative importance of each metric inunderstanding the evolution of file changes in the Rails project. Using hierarchical analysis, we found that code churn, number of past changes, and number of developers explain the evolution of changes in the Rails project better than Social NetworkAnalysis (SNA) metrics. Considering the relative importance of each predictor, wegot the same results. We also conducted a factor analysis and found that social metrics contribute to explain a group of files different from those explained by process metrics

HISTORICAL ANALYSIS OF MESSAGE CONTENTS TO RECOMMEND ISSUES TO OPEN SOURCE SOFTWARE CONTRIBUTORS

Developers of distributed open source projects make use of issue tracker tools to coordinate their work. These tools store valuable information, maintaining a log of relevant decisions and bug solutions. Finding the appropriate issues to contribute can be hard, as the high volume of data increases contributors’ overhead. This paper shows the importance of the content of issue tracker discussions in an open source project to build a classifier to predict the participation of a contributor in an issue. To design this prediction model, we used two machine learning algorithms called Naïve Bayes and J48. We used data from the Apache Hadoop Commons project to evaluate the use of the algorithms. By applying machine learning algorithms to the ten most active contributors of this project, we achieved an average recall of 66.82% for Naïve Bayes and 53.02% using J48. We achieved 64.31% of precision and 90.27% of accuracy using J48. We also conducted an exploratory study with five contributors that took part in fewer issues and achieved 77.41% of precision, 48% of recall, and 98.84% accuracy using J48 algorithm. The results indicate that the content of comments in issues of open source projects is a relevant factor to recommend issues to contributors.Os desenvolvedores de projetos de software livre distribuídos utilizam ferramentas de acompanhamento de pendências para coordenar o seu trabalho. Essas ferramentas armazenam informações importantes, mantendo registro de decisões importantes e soluções para bugs. Decidir sobre que pendências são as mais adequadas para se contribuir pode ser difícil, uma vez que a elevada quantidade de dados aumenta a pressão sobre os desenvolvedores. Este artigo mostra a importância do conteúdo das discussões que ocorrem por meio da ferramenta de acompanhamento de pendências em um projeto de software livre para a construção de um classificador para predizer a participação de um colaborador na solução de um problema. Para projetar este modelo de predição, utilizamos dois algoritmos de aprendizagem de máquina: Naïve Bayes e J48. Utilizamos dados do projeto Apache Hadoop Commons para avaliar o uso dos algoritmos. Aplicando algoritmos de aprendizado de máquina aos dez desenvolvedores mais ativos no projeto, obtivemos uma média de recall de 66,82% para Naïve Bayes e 53,02% usando J48. Obtivemos 64,31% de precisão e 90,27% de acurácia usando o J48. Também realizamos um estudo exploratório com cinco desenvolvedores que participaram na solução de um volume menor de problemas , obtendo 77,41% de precisão, 48% de recall, e 98,84% de acurácia usando o algoritmo J48. Os resultados indicam que o conteúdo dos comentários em pendências/ problemas em projetos de software livre representam um fator relevante com base no qual recomendar pendências aos desenvolvedores que colaboram com o projeto

Will You Come Back to Contribute? Investigating the Inactivity of OSS Core Developers in GitHub

Several Open Source Software (OSS) projects depend on the continuity of their development communities to remain sustainable. Understanding how developers become inactive or why they take breaks can help communities prevent abandonment and incentivize developers to come back. In this paper, we propose a novel method to identify developers' inactive periods by analyzing the individual rhythm of contributions to the projects. Using this method, we quantitatively analyze the inactivity of core developers in 18 OSS organizations hosted on GitHub. We also survey core developers to receive their feedback about the identified breaks and transitions. Our results show that our method was effective for identifying developers' breaks. About 94% of the surveyed core developers agreed with our state model of inactivity; 71% and 79% of them acknowledged their breaks and state transition, respectively. We also show that all core developers take breaks (at least once) and about a half of them (~45%}) have completely disengaged from a project for at least one year. We also analyzed the probability of transitions to/from inactivity and found that developers who pause their activity have a ~35-55\% chance to return to an active state; yet, if the break lasts for a year or longer, then the probability of resuming activities drops to ~21-26%, with a ~54% chance of complete disengagement. These results may support the creation of policies and mechanisms to make OSS community managers aware of breaks and potential project abandonment.Comment: Empirical Software Engineering, to appea

Why Do Developers Take Breaks from Contributing to OSS Projects? A Preliminary Analysis

Creating a successful and sustainable Open Source Software (OSS) project often depends on the strength and the health of the community behind it. Current literature explains the contributors’ lifecycle, starting with the motivations that drive people to contribute and barriers to joining OSS projects, covering developers’ evolution until they become core members. However, the stages when developers leave the projects are still weakly explored and are not welldefined in existing developers’ lifecycle models. In this position paper, we enrich the knowledge about the leaving stage by identifying sleeping and dead states, representing temporary and permanent brakes that developers take from contributing. We conducted a preliminary set of semi-structured interviews with active developers. We analyzed the answers by focusing on defining and understanding the reasons for the transitions to/from sleeping and dead states. This paper raises new questions that may guide further discussions and research, which may ultimately benefit OSS communities

HISTORICAL ANALYSIS OF MESSAGE CONTENTS TO RECOMMEND ISSUES TO OPEN SOURCE SOFTWARE CONTRIBUTORS

Developers of distributed open source projects make use of issue tracker tools to coordinate their work. These tools store valuable information, maintaining a log of relevant decisions and bug solutions. Finding the appropriate issues to contribute can be hard, as the high volume of data increases contributors’ overhead. This paper shows the importance of the content of issue tracker discussions in an open source project to build a classifier to predict the participation of a contributor in an issue. To design this prediction model, we used two machine learning algorithms called Naïve Bayes and J48. We used data from the Apache Hadoop Commons project to evaluate the use of the algorithms. By applying machine learning algorithms to the ten most active contributors of this project, we achieved an average recall of 66.82% for Naïve Bayes and 53.02% using J48. We achieved 64.31% of precision and 90.27% of accuracy using J48. We also conducted an exploratory study with five contributors that took part in fewer issues and achieved 77.41% of precision, 48% of recall, and 98.84% accuracy using J48 algorithm. The results indicate that the content of comments in issues of open source projects is a relevant factor to recommend issues to contributors.Os desenvolvedores de projetos de software livre distribuídos utilizam ferramentas de acompanhamento de pendências para coordenar o seu trabalho. Essas ferramentas armazenam informações importantes, mantendo registro de decisões importantes e soluções para bugs. Decidir sobre que pendências são as mais adequadas para se contribuir pode ser difícil, uma vez que a elevada quantidade de dados aumenta a pressão sobre os desenvolvedores. Este artigo mostra a importância do conteúdo das discussões que ocorrem por meio da ferramenta de acompanhamento de pendências em um projeto de software livre para a construção de um classificador para predizer a participação de um colaborador na solução de um problema. Para projetar este modelo de predição, utilizamos dois algoritmos de aprendizagem de máquina: Naïve Bayes e J48. Utilizamos dados do projeto Apache Hadoop Commons para avaliar o uso dos algoritmos. Aplicando algoritmos de aprendizado de máquina aos dez desenvolvedores mais ativos no projeto, obtivemos uma média de recall de 66,82% para Naïve Bayes e 53,02% usando J48. Obtivemos 64,31% de precisão e 90,27% de acurácia usando o J48. Também realizamos um estudo exploratório com cinco desenvolvedores que participaram na solução de um volume menor de problemas , obtendo 77,41% de precisão, 48% de recall, e 98,84% de acurácia usando o algoritmo J48. Os resultados indicam que o conteúdo dos comentários em pendências/ problemas em projetos de software livre representam um fator relevante com base no qual recomendar pendências aos desenvolvedores que colaboram com o projeto