Automatic code review by learning the revision of source code

Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i.e., the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall correlation between two pieces of source code, but not for the “difference” between two pieces of source code. In this paper, we propose a novel deep model named DACE for automatic code review. Such a model is able to learn revision features by contrasting the revised hunks from the original and revised source code with respect to the code context containing the hunks. Experimental results on six open source software projects indicate by learning the revision features, DACE can outperform the competing approaches in automatic code review

Towards Automatic Identification of Violation Symptoms of Architecture Erosion

Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation drifts away from the intended architecture. To prevent this, development teams need to understand early enough the symptoms of erosion, and particularly violations of the intended architecture. One way to achieve this, is through the automatic identification of architecture violations from textual artifacts, and particularly code reviews. In this paper, we developed 15 machine learning-based and 4 deep learning-based classifiers with three pre-trained word embeddings to identify violation symptoms of architecture erosion from developer discussions in code reviews. Specifically, we looked at code review comments from four large open-source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We then conducted a survey to acquire feedback from the involved participants who discussed architecture violations in code reviews, to validate the usefulness of our trained classifiers. The results show that the SVM classifier based on word2vec pre-trained word embedding performs the best with an F1-score of 0.779. In most cases, classifiers with the fastText pre-trained word embedding model can achieve relatively good performance. Furthermore, 200-dimensional pre-trained word embedding models outperform classifiers that use 100 and 300-dimensional models. In addition, an ensemble classifier based on the majority voting strategy can further enhance the classifier and outperforms the individual classifiers. Finally, an online survey of the involved developers reveals that the violation symptoms identified by our approaches have practical value and can provide early warnings for impending architecture erosion.Comment: 20 pages, 4 images, 7 tables, Revision submitted to TSE (2023

Mining Fix Patterns for FindBugs Violations

In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin

Untangling Fine-Grained Code Changes

After working for some time, developers commit their code changes to a version control system. When doing so, they often bundle unrelated changes (e.g., bug fix and refactoring) in a single commit, thus creating a so-called tangled commit. Sharing tangled commits is problematic because it makes review, reversion, and integration of these commits harder and historical analyses of the project less reliable. Researchers have worked at untangling existing commits, i.e., finding which part of a commit relates to which task. In this paper, we contribute to this line of work in two ways: (1) A publicly available dataset of untangled code changes, created with the help of two developers who accurately split their code changes into self contained tasks over a period of four months; (2) a novel approach, EpiceaUntangler, to help developers share untangled commits (aka. atomic commits) by using fine-grained code change information. EpiceaUntangler is based and tested on the publicly available dataset, and further evaluated by deploying it to 7 developers, who used it for 2 weeks. We recorded a median success rate of 91% and average one of 75%, in automatically creating clusters of untangled fine-grained code changes