Will it pass? Predicting the outcome of a source code review

doi:10.3906/elk-1707-173

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Automating code reviews. Gerede and Mazan [112] have proposed to train a classifier on whether a change request is likely to be accepted or not. Knowing in advance the likelihood of a rejected change request would reduce the review effort as those changes would not even reach the reviewing stage.…”

Section: Mcr Themes and Contributionsmentioning

confidence: 99%

Modern Code Reviews—Survey of Literature and Practice

Badampudi

Unterkalmsteiner

Britto

2023

ACM Trans. Softw. Eng. Methodol.

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Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist; it is unknown whether the research community has targeted themes that practitioners consider important. Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners’ opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues. Method: We conducted a systematic mapping study to survey state-of-the-art until and including 2021, employed the Q-Methodology to analyze the practitioners’ perception of the relevance of MCR research, and analyzed the primary studies’ research impact. Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor- and support systems-related research. Conclusion: These results indicate a misalignment between the state-of-the-art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda, which can potentially increase the impact of MCR research.

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Section: Mcr Themes and Contributionsmentioning

confidence: 99%

Modern Code Reviews—Survey of Literature and Practice

Badampudi

Unterkalmsteiner

Britto

2023

ACM Trans. Softw. Eng. Methodol.

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“…Finally, Cagdas Evren Gerede et al (2018) [28] conducted a comprehensive study exploring various machine learning techniques to predict whether code reviews (CRs) on the Geritt platform would undergo revisions before approval. Their research focused on the Android project hosted on Geritt and involved the collection of code review data for 11,633 CRs made between October 2008 and January 2012.…”

Section: Predictive Techniques For Pr Outcomementioning

confidence: 99%

“…Studies on the comprehensive examination of the code review process were conducted by Jason Tsay et al [21], Jiaxin Zhu et al [22], Oleksii Kononenko et al [5,23], Zhi-Xing Li [24], and Yue Yu et al [25]. Outside of the predictive approaches for PR review outcomes proposed by Tapajit Dey et al [26], Jing Jiang et al [27], and C ¸agdaş Evren Gerede et al [28], there has been very limited research focusing on this aspect.…”

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confidence: 99%

Reinforcement Learning for GitHub Pull Request Predictions: Analyzing Development Dynamics

Joshi

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In the rapidly changing software development field, the pull-based model, supported by tools like GitHub, plays a pivotal role in collaborations. Understanding factors influencing this model is crucial for process enhancement. This thesis employs two Reinforcement Learning (RL) formalizations to predict Pull Request (PR) outcomes. The first utilizes 72 PR characteristics (e.g., PR Size, Test Inclusion, Developers' PR Experience, Programming Language), achieving a G-mean of 0.83. The second focuses solely on PR discussions, attaining a higher G-mean of 0.88. Both RL models outperform established techniques like Random Forest, XGBoost, and Naive Bayes. Additionally, the study explores PR factors and merge time through a survey of 22 developers, identifying key influencers such as PR Size and Reviewer Experience, while also revealing common PR review approaches. Concluding, the study outlines achievements, future directions, and establishes an RL-based PR outcome prediction framework, along with publishing specific datasets. II ACKNOWLEDGEMENTS I would like to begin by expressing my deepest gratitude to my supervisor, Dr. Nafiseh Kahani. Her unwavering support, patience, and guidance from the very start to the end of this study have been invaluable. Her willingness to provide insights, share her wisdom, and invest her time has greatly shaped this research, and I am eternally thankful for her contributions.To my family, who made this journey possible, my sincerest thanks. Being an international student, the opportunity presented by Carleton University has been a truly transformative experience. My father, Baldevbhai, my mother, Jayshriben, and my brother, Jay, have provided constant encouragement and love. Your faith in me has been a driving force behind every success, and I am grateful beyond words. I wish to pay a small tribute to my late grandmothers, Jamnaben and Gangaben. Your memories and teachings continue to guide me, and this work is a testament to the values you instilled in me. To the extended Joshi family, thank you for your unending support and belief in my abilities.Special thanks go to Carleton University's Research Ethics Boards, whose assistance in conducting the online survey and approving the ethics application was instrumental to this research. I am humbled by the contributions of each individual and institution mentioned here, and many others who have influenced this research. This thesis stands as a testament to the collective effort, support, and inspiration that have been generously provided to me. Thank you. III

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An exploratory study on confusion in code reviews

et al. 2021

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Context Code review is a widely used technique of systematic examination of code changes which aims at increasing software quality. Code reviews provide several benefits for the project, including finding bugs, knowledge transfer, and assurance of adherence to project guidelines and coding style. However, code reviews have a major cost: they can delay the merge of the code change, and thus, impact the overall development process. This cost can be even higher if developers do not understand something, i.e., when developers face confusion during the code review. Objective This paper studies the phenomenon of confusion in code reviews. Understanding confusion is an important starting point to help reducing the cost of code reviews and enhance the effectiveness of this practice, and hence, improve the development process. Method We conducted two complementary studies. The first one aimed at identifying the reasons for confusion in code reviews, its impacts, and the coping strategies developers use to deal with it. Then, we surveyed developers to identify the most frequently experienced reasons for confusion, and conducted a systematic mapping study of solutions proposed for those reasons in the scientific literature. Results From the first study, we build a framework with 30 reasons for confusion, 14 impacts, and 13 coping strategies. The results of the systematic mapping study shows 38 articles addressing the most frequent reasons for confusion. From those articles, we found 13 different solutions for confusion proposed in the literature, and five impacts were established related to the most frequent reasons for confusion. Conclusions Based on the solutions identified in the mapping study, or the lack of them, we propose an actionable guideline for developers on how to cope with confusion during code reviews; we also make several suggestions how tool builders can support code reviews. Additionally, we propose a research agenda for researchers studying code reviews.

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Will it pass? Predicting the outcome of a source code review

Cited by 4 publications

References 19 publications

Modern Code Reviews—Survey of Literature and Practice

Modern Code Reviews—Survey of Literature and Practice

Reinforcement Learning for GitHub Pull Request Predictions: Analyzing Development Dynamics

An exploratory study on confusion in code reviews

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