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Understanding the Helpfulness of Stale Bot for pull-based development: An Empirical Study of 20 Large Open-Source Projects

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第2期33卷 1-43页

作者： Khatoonabadi, Sayedhassan Costa, Diego Elias Mujahid, Suhaib Shihab, Emad Concordia Univ Dept Comp Sci & Software Engn Data Driven Anal Software DAS Lab 2155 Guy St Montreal PQ H3H 2L9 Canada Concordia Univ Dept Comp Sci & Software Engn 2155 Guy St Montreal PQ H3H 2L9 Canada Mozilla Corp Montreal PQ Canada

pull Requests (PRs) that are neither progressed nor resolved clutter the list of PRs, making it difficult for the maintainers to manage and prioritize unresolved PRs. To automatically track, follow up, and close such inactive PRs, Stale bot was introduced by GitHub. Despite its increasing adoption, there are ongoing debates on whether using Stale bot alleviates or exacerbates the problem of inactive PRs. To better understand if and how Stale bot helps projects in their pull-based development workflow, we perform an empirical study of 20 large and popular open source projects. We find that Stale bot can help deal with a backlog of unresolved PRs, as the projects closed more PRs within the first few months of adoption. Moreover, Stale bot can help improve the efficiency of the PR review process as the projects reviewed PRs that ended up merged and resolved PRs that ended up closed faster after the adoption. However, Stale bot can also negatively affect the contributors, as the projects experienced a considerable decrease in their number of active contributors after the adoption. Therefore, relying solely on Stale bot to deal with inactive PRs may lead to decreased community engagement and an increased probability of contributor abandonment.

关键词： Software development bots pull request abandonment pull-based development modern code review social coding platforms open source software

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Work Practices and Challenges in pull-based development: The Contributor's Perspective 16

Work Practices and Challenges in Pull-Based Development: The...

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38th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Gousios, Georgios Storey, Margaret-Anne Bacchelli, Alberto Radboud Univ Nijmegen Nijmegen Netherlands Univ Victoria Victoria BC Canada Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450339001

The pull-based development model is an emerging way of contributing to distributed software projects that is gaining enormous popularity within the open source software (OSS) world. Previous work has examined this model by focusing on projects and their owners-we complement it by examining the work practices of project contributors and the challenges they face. We conducted a survey with 645 top contributors to active OSS projects using the pull-based model on GitHub, the prevalent social coding site. We also analyzed traces extracted from corresponding GitHub repositories. Our research shows that: contributors have a strong interest in maintaining awareness of project status to get inspiration and avoid duplicating work, but they do not actively propagate information;communication within pull requests is reportedly limited to low-level concerns and contributors often use communication channels external to pull requests;challenges are mostly social in nature, with most reporting poor responsiveness from integrators;and the increased transparency of this setting is a confirmed motivation to contribute. based on these findings, we present recommendations for practitioners to streamline the contribution process and discuss potential future research directions.

关键词： pull-based development open source contribution pull request distributed software development GitHub

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On the Shoulders of Giants: A New Dataset for pull-based development Research 17

On the Shoulders of Giants: A New Dataset for Pull-based Dev...

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IEEE/ACM 17th International Conference on Mining Software Repositories (MSR)

作者： Zhang, Xunhui Rastogi, Ayushi Yu, Yue Natl Univ Def Technol Changsha Peoples R China Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450375177

pull-based development is a widely adopted paradigm for collaboration in distributed software development, attracting eyeballs from both academic and industry. To better study pull-based development model, this paper presents a new dataset containing 96 features collected from 11,230 projects and 3,347,937 pull requests. We describe the creation process and explain the features in details. To the best of our knowledge, our dataset is the most comprehensive and largest one toward a complete picture for pull-based development research.

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

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An Exploratory Study of the pull-based Software development Model 2014

An Exploratory Study of the Pull-Based Software Development ...

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36th International Conference on Software Engineering (ICSE)

作者： Gousios, Georgios Pinzger, Martin van Deursen, Arie Delft Univ Technol Delft Netherlands Univ Klagenfurt Klagenfurt Austria

ISBN: (纸本)9781450327565

The advent of distributed version control systems has led to the development of a new paradigm for distributed software development;instead of pushing changes to a central repository, developers pull them from other repositories and merge them locally. Various code hosting sites, notably Github, have tapped on the opportunity to facilitate pull-based development by offering workflow support tools, such as code reviewing systems and integrated issue trackers. In this work, we explore how pull-based software development works, first on the GHTorrent corpus and then on a carefully selected sample of 291 projects. We find that the pull request model offers fast turnaround, increased opportunities for community engagement and decreased time to incorporate contributions. We show that a relatively small number of factors affect both the decision to merge a pull request and the time to process it. We also examine the reasons for pull request rejection and find that technical ones are only a small minority.

关键词： pull-based development pull request distributed software development empirical software engineering

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ForkXplorer:an approach of fork summary generation

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Frontiers of Computer Science 2022年第2期16卷 1-15页

作者： Zhang ZHANG Xinjun MAO Chao ZHANG Yao LU Key Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

pull-based development has become an important paradigm for distributed software *** this model,each developer independently works on a copied repository(i.e.,a fork)from the central *** is essential for developers to maintain awareness of the state of other forks to improve collaboration *** this paper,we propose a method to automatically generate a summary of a *** first use the random forest method to generate the label of a fork,i.e.,feature implementation or a bug *** on the information of the fork-related commits,we then use the TextRank algorithm to generate detailed activity information of the ***,we apply a set of rules to integrate all related information to construct a complete fork *** validate the effectiveness of our method,we conduct 30 groups of manual experiment and 77 groups of case studies on *** propose Fea_(avg)to evaluate the performance of Fea_(avg)the generated fork summary,considering the content accuracy,content integrity,sentence fluency,and label extraction *** results show that the average of of the fork summary generated by this method is *** than 63%of project maintainers and the contributors believe that the fork summary can improve development efficiency.

关键词： open source software pull-based development fork summary distributed cooperative development

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Code smells in pull requests: An exploratory study

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SOFTWARE-PRACTICE & EXPERIENCE 2024年第3期54卷 419-436页

作者： Azeem, Muhammad Ilyas Shafiq, Saad Mashkoor, Atif Egyed, Alexander Univ Luxembourg SnT Ctr Secur Reliabil & Trust Esch Sur Alzette Luxembourg Johannes Kepler Univ Linz Inst Software Syst Engn Linz Austria

The quality of a pull request is the primary factor integrators consider for its acceptance or rejection. Code smells indicate sub-optimal design or implementation choices in the source code that often lead to a fault-prone outcome, threatening the quality of pull requests. This study explores code smells in 21k pull requests from 25 popular Java projects. We find that both accepted (37%) and rejected (44%) pull requests have code smells, affected mainly by god classes and long methods. Besides, we observe that smelly pull requests are more complex and challenging to understand as they have significantly large sizes, long latency times, more discussion and review comments, and are submitted by contributors with less experience. Our results show that features used in previous studies for pull request acceptance prediction could be potentially employed to predict smell in incoming pull requests. We propose a dynamic approach to predict the presence of such code smells in the newly added pull requests. We evaluate our approach on a dataset of 25 Java projects extracted from GitHub. We further conduct a benchmark study to compare the performance of eight machine learning classifiers. Results of the benchmark study show that XGBoost is the best-performing classifier for smell prediction.

关键词： code smell machine learning pull-based development pull requests

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Are You Still Working on This? An Empirical Study on pull Request Abandonment

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第6期48卷 2173-2188页

作者： Li, Zhixing Yu, Yue Wang, Tao Yin, Gang Li, ShanShan Wang, Huaimin Natl Univ Def Technol Coll Comp Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China

The great success of numerous community-based open source software (OSS) is based on volunteers continuously submitting contributions, but ensuring sustainability is a persistent challenge in OSS communities. Although the motivations behind and barriers to OSS contributors' joining and retention have been extensively studied, the impacts of, reasons for and solutions to contribution abandonment at the individual level have not been well studied, especially for pull-based development. To bridge this gap, we present an empirical study on pull request abandonment based on a sizable dataset. We manually examine 321 abandoned pull requests on GitHub and then quantify the manual observations by surveying 710 OSS developers. We find that while the lack of integrators' responsiveness and the lack of contributors' time and interest remain the main reasons that deter contributors from participation, limitations during the processes of patch updating and consensus reaching can also cause abandonment. We also show the significant impacts of pull request abandonment on project management and maintenance. Moreover, we elucidate the strategies used by project integrators to cope with abandoned pull requests and highlight the need for a practical handover mechanism. We discuss the actionable suggestions and implications for OSS practitioners and tool builders, which can help to upgrade the infrastructure and optimize the mechanisms of OSS communities.

关键词： Tools Collaboration Sustainable development Open source software Manuals Maintenance engineering Computer bugs pull request abandonment pull-based development open source software

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Predicting the First Response Latency of Maintainers and Contributors in pull Requests

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第10期50卷 2529-2543页

作者： Khatoonabadi, Sayedhassan Abdellatif, Ahmad Costa, Diego Elias Shihab, Emad Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada Univ Calgary Dept Elect & Software Engn Calgary AB T2N 1N4 Canada

The success of a pull Request (PR) depends on the responsiveness of the maintainers and the contributor during the review process. Being aware of the expected waiting times can lead to better interactions and managed expectations for both the maintainers and the contributor. In this paper, we propose a machine-learning approach to predict the first response latency of the maintainers following the submission of a PR, and the first response latency of the contributor after receiving the first response from the maintainers. We curate a dataset of 20 large and popular open-source projects on GitHub and extract 21 features to characterize projects, contributors, PRs, and review processes. Using these features, we then evaluate seven types of classifiers to identify the best-performing models. We also conduct permutation feature importance and SHAP analyses to understand the importance and the impact of different features on the predicted response latencies. We find that our CatBoost models are the most effective for predicting the first response latencies of both maintainers and contributors. Compared to a dummy classifier that always returns the majority class, these models achieved an average improvement of 29% in AUC-ROC and 51% in AUC-PR for maintainers, as well as 39% in AUC-ROC and 89% in AUC-PR for contributors across the studied projects. The results indicate that our models can aptly predict the first response latencies using the selected features. We also observe that PRs submitted earlier in the week, containing an average number of commits, and with concise descriptions are more likely to receive faster first responses from the maintainers. Similarly, PRs with a lower first response latency from maintainers, that received the first response of maintainers earlier in the week, and containing an average number of commits tend to receive faster first responses from the contributors. Additionally, contributors with a higher acceptance rate and a history of timel

关键词： Reviews Predictive models Feature extraction Software development management History Time measurement Machine learning pull request abandonment pull-based development modern code review social coding open source software

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pull Request Decisions Explained: An Empirical Overview

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第2期49卷 849-871页

作者： Zhang, Xunhui Yu, Yue Gousios, Georgios Rastogi, Ayushi Natl Univ Def Technol Natl Key Lab Parallel Distribut Changsha 410073 Hunan Peoples R China Delft Univ Technol NL-2628 CD Delft Netherlands Univ Groningen Fac Sci & Engn NL-9712 CP Groningen Netherlands

Context: The pull-based development model is widely used in open source projects, leading to the emergence of trends in distributed software development. One aspect that has garnered significant attention concerning pull request decisions is the identification of explanatory factors. Objective: This study builds on a decade of research on pull request decisions and provides further insights. We empirically investigate how factors influence pull request decisions and the scenarios that change the influence of such factors. Method: We identify factors influencing pull request decisions on GitHub through a systematic literature review and infer them by mining archival data. We collect a total of 3,347,937 pull requests with 95 features from 11,230 diverse projects on GitHub. Using these data, we explore the relations among the factors and build mixed effects logistic regression models to empirically explain pull request decisions. Results: Our study shows that a small number of factors explain pull request decisions, with that concerning whether the integrator is the same as or different from the submitter being the most important factor. We also note that the influence of factors on pull request decisions change with a change in context;e.g., the area hotness of pull request is important only in the early stage of project development, however it becomes unimportant for pull request decisions as projects become mature.

关键词： pull-based development pull request decision distributed software development GitHub

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Revealing Software development Work Patterns with PR-Issue Graph Topologies

Proceedings of the ACM on Software Engineering

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Proceedings of the ACM on Software Engineering 2024年第FSE期1卷 2402-2423页

作者： Cleidson R. B. de Souza Emilie Ma Jesse Wong Dongwook Yoon Ivan Beschastnikh Federal University of Pará Belém Brazil University of British Columbia Vancouver Canada

How software developers work and collaborate, and how we can best support them is an important topic for software engineering research. One issue for developers is a limited understanding of work that has been done and is ongoing. Modern systems allow developers to create Issues and pull requests (PRs) to track and structure work. However, developers lack a coherent view that brings together related Issues and PRs. In this paper, we first report on a study of work practices of developers through Issues, PRs, and the links that connect them. Specifically, we mine graphs where the Issues and PRs are nodes, and references (links) between them are the edges. This graph-based approach provides a window into a set of collaborative software engineering practices that have not been previously described. based on a qualitative analysis of 56 GitHub projects, we report on eight types of work practices alongside their respective PR/Issue topologies. Next, inspired by our findings, we developed a tool called WorkflowsExplorer to help developers visualize and study workflow types in their own projects. We evaluated WorkflowsExplorer with 6 developers and report on findings from our interviews. Overall, our work illustrates the value of embracing a topology-focused perspective to investigate collaborative work practices in software development.

关键词： graphs issues pull requests pull-based development work practices

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