Early prediction of merged code changes to prioritize reviewing tasks

Fan, Yuanrui; Xia, Xin; Lo, David; Li, Shanping

doi:10.1007/s10664-018-9602-0

Cited by 63 publications

(60 citation statements)

References 59 publications

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“…A prediction model with the best performance provides an AUC value close to 1. The ROC analysis is robust in case of imbalanced class distributions and asymmetric misclassification costs …”

Section: Experiments Design and Results Analysismentioning

confidence: 99%

Multitask defect prediction

Chen

Xia

et al. 2019

J Software Evolu Process

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Within‐project defect prediction assumes that we have sufficient labeled data from the same project, while cross‐project defect prediction assumes that we have plenty of labeled data from source projects. However, in practice, we might only have limited labeled data from both the source and target projects in some scenarios. In this paper, we want to apply multitask learning to investigate such a new scenario. To our best knowledge, this problem (ie, both the source project and the target project have limited labeled data) has not been thoroughly investigated, and we are the first to propose a novel multitask defect prediction approach mask. mask consists of a differential evolution optimization phase and a multitask learning phase. The former phase aims to find optimal weights for shared and nonshared information in related projects (ie, the target project and its related source projects), while the latter phase builds prediction models for each project simultaneously. To verify the effectiveness of mask, we perform experimental studies on 18 real‐world software projects and compare our approach with four state‐of‐the‐art baseline approaches: single‐task learning (STL), simple combined learning (SCL), Peters filter, and Burak filter. Experimental results show that mask can achieve F1 of 0.397 and AUC of 0.608 on average with a few labeled data (ie, 10% of data). Across the 18 projects, mask can outperform baseline methods significantly in terms of F1 and AUC. Therefore, by utilizing the relatedness among multiple projects, mask can perform significantly better than the state‐of‐the‐art methods. The results confirm that mask is promising for software defect prediction when the source and target projects both have limited training data.

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“…A prediction model with the best performance provides an AUC value close to 1. The ROC analysis is robust in case of imbalanced class distributions and asymmetric misclassification costs …”

Section: Experiments Design and Results Analysismentioning

confidence: 99%

Multitask defect prediction

Chen

Xia

et al. 2019

J Software Evolu Process

Self Cite

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“…In this paper, we investigate merge-conflict prediction by creating a list of nine feature sets that can potentially impact conflicts. Our list is based on previous work in the areas of software merging and code review [5], [9], [13], [34], [35]. Our work is different from all the above in that we use statistical machine learning to create a classifier, for each programming language, that can predict conflicts in unseen merge scenarios.…”

Section: Proactive Conflict Detectionmentioning

confidence: 99%

“…Some other feature sets have a dimension greater than one in order to represent all the needed information; such feature sets would be represented as a vector. For example, feature set #4 is inspired from previous merging and code review studies [34], [35] and indicates code churn. We include this feature set since more code changes may increase the chance of conflicts.…”

Section: B Feature Extractionmentioning

confidence: 99%

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Predicting Merge Conflicts in Collaborative Software Development

Owhadi-Kareshk

Nadi

Rubin

2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background. During collaborative software development, developers often use branches to add features or fix bugs. When merging changes from two branches, conflicts may occur if the changes are inconsistent. Developers need to resolve these conflicts before completing the merge, which is an error-prone and time-consuming process. Early detection of merge conflicts, which warns developers about resolving conflicts before they become large and complicated, is among the ways of dealing with this problem. Existing techniques do this by continuously pulling and merging all combinations of branches in the background to notify developers as soon as a conflict occurs, which is a computationally expensive process. One potential way for reducing this cost is to use a machine-learning based conflict predictor that filters out the merge scenarios that are not likely to have conflicts, i.e. safe merge scenarios.Aims. In this paper, we assess if conflict prediction is feasible.Method. We design a classifier for predicting merge conflicts, based on 9 light-weight Git feature sets. To evaluate our predictor, we perform a large-scale study on 267, 657 merge scenarios from 744 GitHub repositories in seven programming languages.Results. Our results show that we achieve high f1-scores, varying from 0.95 to 0.97 for different programming languages, when predicting safe merge scenarios. The f1-score is between 0.57 and 0.68 for the conflicting merge scenarios.Conclusions. Predicting merge conflicts is feasible in practice, especially in the context of predicting safe merge scenarios as a pre-filtering step for speculative merging.

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“…To create a PR on GitHub, a developer needs to provide a title and can add a free-form text (i.e., a PR description) to describe further what changes are made and/or why they are needed. PR descriptions can help reviewers gain a quick and adequate Corresponding author. understanding of PRs without digging into details and may reduce the possibility of PRs being ignored or rejected [3], [6]. In addition, PR descriptions can help software maintenance and program comprehension tasks [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Pull Request Descriptions

Liu

Xia

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. To alleviate this problem, we propose an approach to automatically generate PR descriptions based on the commit messages and the added source code comments in the PRs. We regard this problem as a text summarization problem and solve it using a novel sequence-to-sequence model. To cope with out-of-vocabulary words in software artifacts and bridge the gap between the training loss function of the sequence-tosequence model and the evaluation metric ROUGE, which has been shown to correspond to human evaluation, we integrate the pointer generator and directly optimize for ROUGE using reinforcement learning and a special loss function. We build a dataset with over 41K PRs and evaluate our approach on this dataset through ROUGE and a human evaluation. Our evaluation results show that our approach outperforms two baselines by significant margins.

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Early prediction of merged code changes to prioritize reviewing tasks

Cited by 63 publications

References 59 publications

Multitask defect prediction

Multitask defect prediction

Predicting Merge Conflicts in Collaborative Software Development

Automatic Generation of Pull Request Descriptions

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