Everton L. G. Alves

Flávia Coelho, Nikolaos Tsantalis, Tiago Massoni et al.

Background: Pull-based development has shaped the practice of Modern Code Review (MCR), in which reviewers can contribute code improvements, such as refactorings, through comments and commits in Pull Requests (PRs). Past MCR studies uniformly treat all PRs, regardless of whether they induce refactoring or not. We define a PR as refactoring-inducing, when refactoring edits are performed after the initial commit(s), as either a result of discussion among reviewers or spontaneous actions carried out by the PR developer. Aims: This mixed study (quantitative and qualitative) explores code reviewing-related aspects intending to characterize refactoring-inducing PRs. Method: We hypothesize that refactoring-inducing PRs have distinct characteristics than non-refactoring-inducing ones and thus deserve special attention and treatment from researchers, practitioners, and tool builders. To investigate our hypothesis, we mined a sample of 1,845 Apache's merged PRs from GitHub, mined refactoring edits in these PRs, and ran a comparative study between refactoring-inducing and non-refactoring-inducing PRs. We also manually examined 2,096 review comments and 1,891 detected refactorings from 228 refactoring-inducing PRs. Results: We found 30.2% of refactoring-inducing PRs in our sample and that they significantly differ from non-refactoring-inducing ones in terms of number of commits, code churn, number of file changes, number of review comments, length of discussion, and time to merge. However, we found no statistical evidence that the number of reviewers is related to refactoring-inducement. Our qualitative analysis revealed that at least one refactoring edit was induced by review in 133 (58.3%) of the refactoring-inducing PRs examined. Conclusions: Our findings suggest directions for researchers, practitioners, and tool builders to improve practices around pull-based code review.

João Felipe Silva Ouriques, Emanuela Gadelha Cartaxo, Everton Leandro Galdino Alves et al.

Test Case Prioritization (TCP) techniques aim at proposing new test case execution orders to favor the achievement of certain testing goal, such as fault detection. Current TCP research focus mainly on code-based regression testing; however in the Model-Based Testing (MBT) context, we still need more investigation. General TCP techniques do not use historical information, since this information is often unavailable. Therefore, techniques use different sources of information to guide prioritization. We propose a novel technique that guides its operation using provided hints, the Hint-Based Adaptive Random Prioritization - HARP. Hints are indications or suggestions provided by developers about error-prone functionalities. As hints may be hard to collect automatically, we also propose an approach of collecting them. To validate our findings, we performed an experiment measuring the effect of introducing hints to HARP. It shows that hints can improve HARP's performance comparing to its baseline. Then, we investigated the ability of developers/managers to provide good hints and used them in a case study. This analysis showed that developers and managers are able to provide useful hints, which improves HARP's fault detection comparing to its baseline. Nonetheless, the provided hints should be consensual among the development team members.