The advent of distributed version control systems has led to the development of a new paradigm for distributed software development; instead of pushing changes to a central repository, developers pull them from other repositories and merge them locally. Various code hosting sites, notably Github, have tapped on the opportunity to facilitate pull-based development by offering workflow support tools, such as code reviewing systems and integrated issue trackers. In this work, we explore how pull-based software development works, first on the GHTorrent corpus and then on a carefully selected sample of 291 projects. We find that the pull request model offers fast turnaround, increased opportunities for community engagement and decreased time to incorporate contributions. We show that a relatively small number of factors affect both the decision to merge a pull request and the time to process it. We also examine the reasons for pull request rejection and find that technical ones are only a small minority.

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Abstract

Software teams should follow a well defined goal and keep their work focused. Work fragmentation is bad for efficiency and quality. In this paper we empirically investigate the relationship between the fragmentation of developer contributions and the number of post-release failures. Our approach is to represent developer contributions with a developer-module network that we call contribution network. We use network centrality measures to measure the degree of fragmentation of developer contributions. Fragmentation is determined by the centrality of software modules in the contribution network. Our claim is that central software modules are more likely to be failure-prone than modules located in surrounding areas of the network. We analyze this hypothesis by exploring the network centrality of Microsoft Windows Vista binaries using several network centrality measures as well as linear and logistic regression analysis. In particular, we investigate which centrality measures are significant to predict the probability and number of post-release failures. Results of our experiments show that central modules are more failure-prone than modules located in surrounding areas of the network. Results further confirm that number of authors and number of commits are significant predictors for the probability of post-release failures. For predicting the number of post-release failures the closeness centrality measure is most significant. Fragmentation is determined by the centrality of software modules in the contribution network. Our claim is that central software modules are more likely to be failure-prone than modules located in surrounding areas of the network. We analyze this hypothesis by exploring the network centrality of Microsoft Windows Vista binaries using several network centrality measures as well as linear and logistic regression analysis. In particular, we investigate which centrality measures are significant to predict the probability and number of post-release failures. Results of our experiments show that central modules are more failure-prone than modules located in surrounding areas of the network. Results further confirm that number of authors and number of commits are significant predictors for the probability of post-release failures. For predicting the number of post-release failures the closeness centrality measure is most significant. Can Developer-Module Networks Predict Failures?

Researchers proposed a wide range of approaches to build effective bug prediction models that take into account multiple aspects of the software development process. Such models achieved good prediction performance, guiding developers towards those parts of their system where a large share of bugs can be expected. However, most of those approaches predict bugs on file-level. This often leaves developers with a considerable amount of effort to examine all methods of a file until a bug is located. This particular problem is reinforced by the fact that large files are typically predicted as the most bugprone. In this paper, we present bug prediction models at the level of individual methods rather than at file-level. This increases the granularity of the prediction and thus reduces manual inspection efforts for developers. The models are based on change metrics and source code metrics that are typically used in bug prediction. Our experiments-performed on 21 Java open-source (sub-)systems-show that our prediction models reach a precision and recall of 84% and 88%, respectively. Furthermore, the results indicate that change metrics significantly outperform source code metrics. Method-Level Bug Prediction ABSTRACTResearchers proposed a wide range of approaches to build effective bug prediction models that take into account multiple aspects of the software development process. Such models achieved good prediction performance, guiding developers towards those parts of their system where a large share of bugs can be expected. However, most of those approaches predict bugs on file-level. This often leaves developers with a considerable amount of effort to examine all methods of a file until a bug is located. This particular problem is reinforced by the fact that large files are typically predicted as the most bug-prone. In this paper, we present bug prediction models at the level of individual methods rather than at file-level. This increases the granularity of the prediction and thus reduces manual inspection efforts for developers. The models are based on change metrics and source code metrics that are typically used in bug prediction. Our experiments-performed on 21 Java open-source (sub-)systems-show that our prediction models reach a precision and recall of 84% and 88%, respectively. Furthermore, the results indicate that change metrics significantly outperform source code metrics.