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26th Asia-Pacific software Engineering Conference (APSEC)

作者： Syed-Mohamad, Sharifah Mashita Akhir, Nur Shazwani Md. Univ Sains Malaysia Sch Comp Sci George Town Malaysia

ISBN: (纸本)9781728146485

pull-based software development is a distributed development model that offers an opportunity to review a pull request before it gets merged into the main repository. A pull request addresses new features, bug fixing, and maintenance issues submitted by both integrators or contributors. It appears that many empirical studies are conducted to discover how pull request evaluation is done, and to our knowledge, limited research exists for assessing release readiness of pull requests. Studies also reported that the failure rate of pull-requests rapidly increases when there are many forks created. It is therefore, questions worth exploring are whether the code review really contributing to the code quality, and how to determine the release readiness of pull requests? In our previous work, test and defect coverage-based analytics model (TDCAM) has been proven to be suitable to determine the readiness of releases for software that is rapidly evolving, in which this is also a characteristic of pull-based software development. In this paper, the TDCAM has been extended to include pull request coverage indicators. The proposed model, namely as SoReady and the visualization analysis presented herein has enabled five developers in a commercial setting to make informed and evidence- based decisions regarding the test status of each pull request and overall reliability of an open source software through a prototype dashboard.

关键词： pull-based software development agile software release readiness pull requests test coverage defect coverage metrics test analytics

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Towards Auto-labelling Issue Reports for pull-based software development using Text Mining Approach 5

Towards Auto-labelling Issue Reports for Pull-Based Software...

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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Fazayeli, Hassan Syed-Mohamad, Sharifah Mashita Akhir, Nur Shazwani Md Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

In recent years continuous integration has become an important practice for pull-based software development. It helps developers make contributions flexibly to an isolated copy of the project's repository, create a pull request that represent such changes, and submit it to the project for evaluation. Despite its test centric approach, the ability of teams to scale up distributed development is limited by the manually demanding task of adding and updating labels to an issue report. Automating this step enables a project to produce quality results. We categorized issue reports to derive a specific classification of issue reports of pull-based development projects. Given that manual classification of issues is a tedious task and needs an expert domain knowledge, auto-classification is extremely needed. We considered different strategies and developed diverse text analytics techniques for automatic labelling issue reports of a GitHub's project. We applied 748, SMO, Naive Byes, and LibSVM algorithms so that different classification models are produced. Our results showed that SMO is able to label the issue reports with accuracy of 87.15 and F-measure of 0.869, when both the title and the body description of issues are considered in a balanced binary classification model. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Continuous Integration GitHub Issue Reports pull-based software development Text Classification

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Towards Auto-labelling Issue Reports for pull-based software development using Text Mining Approach

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Procedia Computer Science 2019年 161卷 585-592页

作者： Hassan Fazayeli Sharifah Mashita Syed-Mohamad Nur Shazwani Md Akhir School of Computer Science Universiti Sains Malaysia 11800 USM Pulau Pinang Malaysia

In recent years continuous integration has become an important practice for pull-based software development. It helps developers make contributions flexibly to an isolated copy of the project’s repository, create a pull request that represent such changes, and submit it to the project for evaluation. Despite its test centric approach, the ability of teams to scale up distributed development is limited by the manually demanding task of adding and updating labels to an issue report. Automating this step enables a project to produce quality results. We categorized issue reports to derive a specific classification of issue reports of pull-based development projects. Given that manual classification of issues is a tedious task and needs an expert domain knowledge, auto-classification is extremely needed. We considered different strategies and developed diverse text analytics techniques for automatic labelling issue reports of a GitHub’s project. We applied J48, SMO, Naïve Byes, and LibSVM algorithms so that different classification models are produced. Our results showed that SMO is able to label the issue reports with accuracy of 87.15 and F-measure of 0.869, when both the title and the body description of issues are considered in a balanced binary classification model.

关键词： Continuous Integration GitHub Issue Reports pull-based software development Text Classification

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ConE: A Concurrent Edit Detection Tool for Large-scale software development

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ACM TRANSACTIONS ON software ENGINEERING AND METHODOLOGY 2022年第2期31卷 22-22页

作者： Maddila, Chandra Nagappan, Nachiappan Bird, Christian Gousios, Georgios van Deursen, Arie Microsoft Res 14820 NE 36th St Redmond WA 98052 USA Facebook 1 Hacker Way Menlo Pk CA 94025 USA Delft Univ Technol Bldg 28Van Mourik Broekmanweg 6 NL-2628 XE Delft Netherlands

Modern, complex software systems are being continuously extended and adjusted. The developers responsible for this may come from different teams or organizations, and may be distributed over the world. This may make it difficult to keep track of what other developers are doing, which may result in multiple developers concurrently editing the same code areas. This, in turn, may lead to hard-to-merge changes or even merge conflicts, logical bugs that are difficult to detect, duplication of work, and wasted developer productivity. To address this, we explore the extent of this problem in the pull-request-based software development model. We study half a year of changes made to six large repositories in Microsoft in which at least 1,000 pull requests are created each month. We find that files concurrently edited in different pull requests are more likely to introduce bugs. Motivated by these findings, we design, implement, and deploy a service named Concurrent Edit Detector (ConE) that proactively detects pull requests containing concurrent edits, to help mitigate the problems caused by them. ConE has been designed to scale, and to minimize false alarms while still flagging relevant concurrently edited files. Key concepts of ConE include the detection of the Extent of Overlap between pull requests, and the identification of Rarely Concurrently Edited Files. To evaluate ConE, we report on its operational deployment on 234 repositories inside Microsoft. ConE assessed 26,000 pull requests and made 775 recommendations about conflicting changes, which were rated as useful in over 70% (554) of the cases. From interviews with 48 users, we learned that they believed ConE would save time in conflict resolution and avoiding duplicate work, and that over 90% intend to keep using the service on a daily basis.

关键词： pull-based software development pull request merge conflict distributed software development

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CoreDevRec： Automatic Core Member Recommendation for Contribution Evaluation

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Journal of Computer Science & Technology 2015年第5期30卷 998-1016页

作者：蒋竞贺佳欢陈学渊 State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

The pull-based software development helps developers make contributions flexibly and efficiently. Core members evaluate code changes submitted by contributors, and decide whether to merge these code changes into repositories or not. Ideally, code changes are assigned to core members and evaluated within a short time after their submission. However, in reality, some popular projects receive many pull requests, and core members have difficulties in choosing pull requests which are to be evaluated. Therefore, there is a growing need for automatic core member recommendation, which improves the evaluation process. In this paper, we investigate pull requests with manual assignment. Results show that 3.2%～40.6% of pull requests are manually assigned to specific core members. To assist with the manual assignment, we propose CoreDevRec to recommend core members for contribution evaluation in GitHub. CoreDevRec uses support vector machines to analyze different kinds of features, including file paths of modified codes, relationships between contributors and core members, and activeness of core members. We evaluate CoreDevRec on 18 651 pull requests of five popular projects in GitHub. Results show that CoreDevRec achieves accuracy from 72.9% to 93.5% for top 3 recommendation. In comparison with a baseline approach, CoreDevRec improves the accuracy from 18.7% to 81.3% for top 3 recommendation. Moreover, CoreDevRec even has higher accuracy than manual assignment in the project TrinityCore. We believe that CoreDevRec can improve the assignment of pull requests.

关键词： core member recommendation contribution evaluation pull-based software development

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Who should comment on this pull request? Analyzing attributes for more accurate commenter recommendation in pull-based development

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INFORMATION AND software TECHNOLOGY 2017年 84卷 48-62页

作者： Jiang, Jing Yang, Yun He, Jiahuan Blanc, Xavier Zhang, Li Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Univ Bordeaux LaBRI UMR 5800 F-33400 Talence France

Context: The pull-based software development helps developers make contributions flexibly and efficiently. Commenters freely discuss code changes and provide suggestions. Core members make decision of pull requests. Both commenters and core members are reviewers in the evaluation of pull requests. Since some popular projects receive many pull requests, commenters may not notice new pull requests in time, and even ignore appropriate pull requests. Objective: Our objective in this paper is to analyze attributes that affect the precision and recall of commenter prediction, and choose appropriate attributes to build commenter recommendation approach. Method: We collect 19,543 pull requests, 206,664 comments and 4817 commenters from 8 popular projects in GitHub. We build approaches based on different attributes, including activeness, text similarity, file similarity and social relation. We also build composite approaches, including time-based text similarity, time-based file similarity and time-based social relation. The time-based social relation approach is the state-of-the-art approach proposed by Yu et al. Then we compare precision and recall of different approaches. Results: We find that for 8 projects, the activeness based approach achieves the top-3 precision of 0.276, 0.386, 0.389, 0.516, 0.322, 0.572, 0.428, 0.402, and achieves the top-3 recall of 0.475, 0.593, 0.613, 0.66, 0.644, 0.791, 0.714, 0.65, which outperforms approaches based on text similarity, file similarity or social relation by a substantial margin. Moreover, the activeness based approach achieves better precision and recall than composite approaches. In comparison with the state-of-the-art approach, the activeness based approach improves the top-3 precision by 178.788%, 30.41%, 25.08%, 41.76%, 49.07%, 32.71%, 25.15%, 78.67%, and improves the top-3 recall by 196.875%, 36.32%, 29.05%, 46.02%, 43.43%, 27.79%, 25.483%, 79.06% for 8 projects. Conclusion: The activeness is the most important attribute i

关键词： Commenter recommendation Reviewer recommendation Attribute selection pull-based software development

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Nudge: Accelerating Overdue pull Requests toward Completion

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ACM TRANSACTIONS ON software ENGINEERING AND METHODOLOGY 2023年第2期32卷 1-30页

作者： Maddila, Chandra Upadrasta, Sai Surya Bansal, Chetan Nagappan, Nachiappan Gousios, Georgios van Deursen, Arie Microsoft Res 14820 NE 36th St Redmond WA 98052 USA Microsoft Res Vigyan 1st Floor9 Lavelle Rd Bengaluru India Delft Univ Technol Bldg 28Van Mourik Broekmanweg 6 NL-2628 XE Delft Netherlands

pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests toward completion by reminding the author or the reviewer(s) to engage with their overdue pull requests. First, we use models based on effort estimation and machine learning to predict the completion time for a given pull request. Second, we use activity detection to filter out pull requests that may be overdue but for which sufficient action is taking place nonetheless. Last, we use actor identification to understand who the blocker of the pull request is and nudge the appropriate actor (author or reviewer(s)). The key novelty of Nudge is that it succeeds in reducing pull request resolution time, while ensuring that developers perceive the notifications sent as useful, at the scale of thousands of repositories. In a randomized trial on 147 repositories in use at Microsoft, Nudge was able to reduce pull request resolution time by 60% for 8,500 pull requests, when compared to overdue pull requests for which Nudge did not send a notification. Furthermore, developers receiving Nudge notifications resolved 73% of these notifications as positive. We observed similar results when scaling up the deployment of Nudge to 8,000 repositories at Microsoft, for which Nudge sent 210,000 notifications during a full year. This demonstrates Nudge's ability to scale to thousands of repositories. Last, our qualitative analysis of a selection of Nudge notifications indicates areas for future research, such as taking dependencies among pull requests and developer availability into account.

关键词： pull-based software development pull request merge conflict distributed software development

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Automatic assignment of integrators to pull requests: The importance of selecting appropriate attributes

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JOURNAL OF SYSTEMS AND software 2018年 144卷 181-196页

作者： de Lima Junior, Manoel Limeira Soares, Daricelio Moreira Plastino, Alexandre Murta, Leonardo Univ Fed Acre Ctr Exact & Technol Sci Rio Branco AC Brazil Fluminense Fed Univ Inst Comp Niteroi RJ Brazil

In open-source projects that adopt the pull-based development workflow, a core developer needs to analyze the contribution received via pull requests and decide on integrating it or not in the repository. However, this process is time-consuming, leading to an increasing number of pull requests left to be analyzed. Consequently, the assignment of suitable integrators to pull requests becomes an important step in the pull-based development workflow. Classification methods have already been used to recommend integrators, based on different sets of predictive attributes. The main contribution of this paper is to identify a set of attributes that can improve the performance of the integrator prediction task reported in the literature. To do so, we first evaluate different sets of attributes used by previous studies with different classification algorithms. Besides, we explore attribute selection strategies on an extended set of attributes composed not only by the attributes already used in the literature but also new attributes we consider relevant to the problem. Experiments with 32 open-source projects evidenced that after applying attribute selection strategies and, consequently, identifying a more suitable set of attributes, the recommendation has achieved normalized improvements 5 4% higher than the state-of-the-art.

关键词： Integrator assignment pull-based software development Distributed software development

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CTCPPre: A prediction method for accepted pull requests in GitHub

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Journal of Central South University 2020年第2期27卷 449-468页

作者： JIANG Jing ZHENG Jia-teng YANG Yun ZHANG Li State Key Laboratory of Software Development Environment Beihang University

As the popularity of open source projects,the volume of incoming pull requests is too large,which puts heavy burden on integrators who are responsible for accepting or rejecting pull *** accepted pull request prediction approach can help integrators by allowing them either to enforce an immediate rejection of code changes or allocate more resources to overcome the *** this paper,an approach CTCPPre is proposed to predict the accepted pull requests in *** mainly considers code features of modified changes,text features of pull requests’description,contributor features of developers’previous behaviors,and project features of development *** effectiveness of CTCPPre on 28 projects containing 221096 pull requests is *** results show that CTCPPre has good performances by achieving accuracy of 0.82,AUC of 0.76 and F1-score of 0.88 on *** is compared with the state of art accepted pull request prediction approach *** average across 28 projects,CTCPPre outperforms RFPredict by 6.64%,16.06%and 4.79%in terms of accuracy,AUC and F1-score,respectively.

关键词： accepted pull request prediction code review GitHub pull-based software development

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