Predicting Design Impactful Changes in Modern Code Review: A Large-Scale Empirical Study

Uchôa, Anderson; Barbosa, C. Hall; Coutinho, Daniel; Oizumi, Willian; Assunção, Wesley K. G.; Vergílio, Silvia Regina; Pereira, Juliana Alves; Oliveira, Anderson; Garcia, Alessandro

doi:10.1109/msr52588.2021.00059

Cited by 24 publications

(13 citation statements)

References 60 publications

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“…Common classical machine learning-based models include classification and regression tree (CART), k-nearest-neighbors (KNN), logistic regression, naive Bayes, random forest, and support vector machine (SVM), among others. They were applied in various general text classification tasks [23,24,25,26], as well as for software engineering tasks in specific [5,27,28,29]. To use these models, features need to be first defined and extracted from textual documents, then fed into the classifier for prediction.…”

Section: Machine Learning For Text Classificationmentioning

confidence: 99%

Incivility Detection in Open Source Code Review and Issue Discussions

Ferreira¹,

Rafiq

Cheng³

2022

SSRN Journal

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Section: Machine Learning For Text Classificationmentioning

confidence: 99%

Incivility Detection in Open Source Code Review and Issue Discussions

Ferreira¹,

Rafiq

Cheng³

2022

SSRN Journal

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“…Additionally, the practices of long discussions and high proportion of review disagreement in code reviews were found to increase design degradation. In their study on predicting design impactful changes in modern code review with technical and/or social aspects, Uchôa et al [88] analyzed reviewed code changes from seven open source projects. By evaluating six machine learning algorithms, the authors found that technical features results in more precise predictions and the use of social features alone also leads to accurate predictions.…”

Section: Related Workmentioning

confidence: 99%

Code Review Practices for Refactoring Changes: An Empirical Study on OpenStack

AlOmar¹,

Chouchen²,

Mkaouer³

et al. 2022

Preprint

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Modern code review is a widely used technique employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure adherence to coding standards and guidelines. During code review, developers may discuss refactoring activities before merging code changes in the code base. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring is being reviewed and what developers care about when they review refactored code. Hence, in this work, we present a quantitative and qualitative study to understand what are the main criteria developers rely on to develop a decision about accepting or rejecting a submitted refactored code, and what makes this process challenging. Through a case study of 11,010 refactoring and non-refactoring reviews spread across OpenStack open-source projects, we find that refactoring-related code reviews take significantly longer to be resolved in terms of code review efforts. Moreover, upon performing a thematic analysis on a significant sample of the refactoring code review discussions, we built a comprehensive taxonomy consisting of 28 refactoring review criteria. We envision our findings reaffirming the necessity of developing accurate and efficient tools and techniques that can assist developers in the review process in the presence of refactorings. CCS CONCEPTS• Software and its engineering → Software evolution; Maintaining software.

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“…The authors used the incidence rates of post-release defects as an indicator and found that poorly reviewed code (e.g., with low review coverage and participation) had a negative impact on software quality. Uchôa et al (2021) investigated whether and how technical (e.g., number of times a file has been changed and types of change) and social (e.g., number of prior code changes submitted by the code owner and centrality of the code owner on the collaboration graph) metrics can be used to predict design impactful changes by analyzing more than 50k code reviews of seven real-world systems.…”

Section: Code Reviews In Software Developmentmentioning

confidence: 99%

Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities

Han¹,

Tahir²,

Peng³

et al. 2022

Preprint

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Code review plays an important role in software quality control. A typical review process involves a careful check of a piece of code in an attempt to detect and locate defects and other quality issues/violations. One type of issue that may impact the quality of software is code smells -i.e., bad coding practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during modern code review. To investigate the concept behind code smells identified in modern code review and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews by analyzing reviews from four large open source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We manually checked a total of 25,415 code review comments obtained by keywords search and random selection; this resulted in the identification of 1,539 smell-related reviews which then allowed the study of the causes of code smells, actions taken against identified smells, time taken to fix identified smells, and reasons why developers ignored fixing identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommen-

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Predicting Design Impactful Changes in Modern Code Review: A Large-Scale Empirical Study

Cited by 24 publications

References 60 publications

Incivility Detection in Open Source Code Review and Issue Discussions

Incivility Detection in Open Source Code Review and Issue Discussions

Code Review Practices for Refactoring Changes: An Empirical Study on OpenStack

Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities

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