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Abstract-Continuous Integration (CI) has become a disruptive innovation in software development: with proper tool support and adoption, positive effects have been demonstrated for pull request throughput and scaling up of project sizes. As any other innovation, adopting CI implies adapting existing practices in order to take full advantage of its potential, and "best practices" to that end have been proposed. Here we study the adaptation and evolution of code writing and submission, issue and pull request closing, and testing practices as TRAVIS CI is adopted by hundreds of established projects on GITHUB. Qualitatively, to help essentialize the quantitative results, we survey a sample of GITHUB developers about their experiences with adopting TRAVIS CI. Our findings suggest a more nuanced picture of how GITHUB teams are adapting to, and benefiting from, continuous integration technology than suggested by prior work.

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Background. Recent years have seen an increasing interest in cross-project defect prediction (CPDP), which aims to apply defect prediction models built on source projects to a target project. Currently, a variety of (complex) CPDP models have been proposed with a promising prediction performance. Problem. Most, if not all, of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. Objective. We aim to investigate how far we have really progressed in the journey by comparing the performance in defect prediction between the existing CPDP models and simple module size models. Method. We first use module size in the target project to build two simple defect prediction models, ManualDown and ManualUp, which do not require any training data from source projects. ManualDown considers a larger module as more defect-prone, while ManualUp considers a smaller module as more defect-prone. Then, we take the following measures to ensure a fair comparison on the performance in defect prediction between the existing CPDP models and the simple module size models: using the same publicly available data sets, using the same performance indicators, and using the prediction performance reported in the original cross-project defect prediction studies. Result. The simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature, including many newly proposed models. Conclusion. The results caution us that, if the prediction performance is the goal, the real progress in CPDP is not being achieved as it might have been envisaged. We hence recommend that future studies should include ManualDown/ManualUp as the baseline models for comparison when developing new CPDP models to predict defects in a complete target project.

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Many studies have investigated the relationships between object-oriented (OO) metrics and change-proneness and conclude that OO metrics are able to predict the extent of change of a class across the versions of a system. However, there is a need to re-examine this subject for two reasons. First, most studies only analyze a small number of OO metrics and, therefore, it is not clear whether this conclusion is applicable to most, if not all, OO metrics. Second, most studies only uses relatively few systems to investigate the relationships between OO metrics and change-proneness and, therefore, it is not clear whether this conclusion can be generalized to other systems. In this paper, based on 102 Java systems, we employ statistical meta-analysis techniques to investigate the ability of 62 OO metrics to predict change-proneness. In our context, a class which is changed in the next version of a system is called change-prone and not change-prone otherwise. The investigated OO metrics cover four metric dimensions, including 7 size metrics, 18 cohesion metrics, 20 coupling metrics, and 17 inheritance metrics. We use AUC (the area under a relative operating characteristic, ROC) to evaluate the predictive effectiveness of OO metrics. For each OO metric, we first compute AUCs and the corresponding variances for individual systems. Then, we employ a random-effect model to compute the average AUC over all systems. Finally, we perform a sensitivity analysis to investigate whether the AUC result from the random-effect model is robust to the data selection bias in this study. Our results from random-effect models reveal that: (1) size metrics exhibit moderate or almost moderate ability in discriminating between change-prone and not change-prone classes; (2) coupling and cohesion metrics generally have a lower predictive ability Empir Software Eng (compared to size metrics; and (3) inheritance metrics have a poor ability to discriminate between change-prone and not change-prone classes. Our results from sensitivity analyses show that these conclusions reached are not substantially influenced by the data selection bias.

Background . The extent of the potentially confounding effect of class size in the fault prediction context is not clear, nor is the method to remove the potentially confounding effect, or the influence of this removal on the performance of fault-proneness prediction models. Objective . We aim to provide an in-depth understanding of the effect of class size on the true associations between object-oriented metrics and fault-proneness. Method . We first employ statistical methods to examine the extent of the potentially confounding effect of class size in the fault prediction context. After that, we propose a linear regression-based method to remove the potentially confounding effect. Finally, we empirically investigate whether this removal could improve the prediction performance of fault-proneness prediction models. Results . Based on open-source software systems, we found: (a) the confounding effect of class size on the associations between object-oriented metrics and fault-proneness in general exists; (b) the proposed linear regression-based method can effectively remove the confounding effect; and (c) after removing the confounding effect, the prediction performance of fault prediction models with respect to both ranking and classification can in general be significantly improved. Conclusion . We should remove the confounding effect of class size when building fault prediction models.