ADCR: An Adaptive TOOL to select ”Appropriate Developer for Code Review” based on Code Context

Sadman, Nafiz; Ahsan, Md Manjurul; Mahmud, M. A. Parvez

doi:10.1109/uemcon51285.2020.9298102

Cited by 2 publications

(4 citation statements)

References 20 publications

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“…Another interesting direction is to focus recommend reviewers that will ensure code base knowledge distribution [86,176,207]. Finally, some studies have included balancing review workload as an objective [43,49,86,230] In relation to how the predictors are used to recommend code reviewers, many employ traditional approaches (e.g., cosine similarity), while some use machine learning techniques, such as Random Forest [92], Naive Bayes [92,235], Support Vector Machines [144,276], Collaborative Filtering [87,230], Deep Neural Networks [222,274], or model reviewer recommendation as an optimization problem [43,86,187,207,211].…”

Section: Mcr Themes and Contributionsmentioning

confidence: 99%

“…Appropriate reviewer selection -The primary studies focus on identifying "good reviewers" based on certain predictors such as pull request content similarity [145,211,268,275]. However, how much do "good reviewers" differ in review performance from "bad reviewers"?…”

Section: Reviewer Selectionmentioning

confidence: 99%

“…Most approaches recommend code reviewers based on the similarity between files modified or reviewed by each developer and the files of a new pull request (path similarity) [87,106,122,144,145,158,187,198,204,222,230,238,242,268,271,273,275,279]. Some studies include other 107:15 predictors such as previous interactions between submitter and potential reviewers [144,145,187,273,275,285], pull request content similarity [145,211,268,275], contribution to similar files [122,158,231,274], review linkage graphs [132], and developer activeness in a project [144,145,158,211,271]. Another popular predictor to recommend code reviewers is the similarity between the content of previous and new pull requests [145, 152, 166, 268-270, 275, 276].…”

Section: Support Systems For Codementioning

confidence: 99%

“…In relation to how the predictors are used to recommend code reviewers, many employ traditional approaches (e.g., cosine similarity), while some use machine learning techniques, such as Random Forest [92], Naive Bayes [92,235], Support Vector Machines [144,276], Collaborative Filtering [87,230], and Deep Neural Networks [222,274], or model reviewer recommendation as an optimization problem [43,86,187,207,211].…”

Section: Support Systems For Codementioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Reviewer Selectionmentioning

confidence: 99%

Section: Support Systems For Codementioning

confidence: 99%

Section: Support Systems For Codementioning

confidence: 99%

See 2 more Smart Citations

Modern Code Reviews—Survey of Literature and Practice

Badampudi

Unterkalmsteiner

Britto

2023

ACM Trans. Softw. Eng. Methodol.

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Automated Control of the Software Prototype Code Review Based on the Question-Answering Protocols for Requirement Analysis

Lapshov¹

2021

АПУ

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The paper describes the architecture of software tools for automating the management of code review of software prototypes of design solutions that allow to obtain such effects as ensuring interactive interaction between the designer and an expert performing code review, as well as reducing the expert’s time spent on commenting the code by selecting a comment from the list prepared in advance for each requirement from the requirements obtained during the analysis. The tools being developed consist of three main parts. The first part presents requirements in a question-and-answer format including standard comments to be inserted into the prototype code in case of non-compliance with these requirements. The second one is a relational database, which is designed to store the source codes of prototypes aimed at inspection and passed it with expert comments. The third one is a Web-application that allows the designer to send prototype codes for review and see the results of the inspection, and an expert to provide viewing and editing of the prototype source code with the insertion of both standard, pre-prepared comments, and written in free form.

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ADCR: An Adaptive TOOL to select ”Appropriate Developer for Code Review” based on Code Context

Cited by 2 publications

References 20 publications

Modern Code Reviews—Survey of Literature and Practice

Modern Code Reviews—Survey of Literature and Practice

Automated Control of the Software Prototype Code Review Based on the Question-Answering Protocols for Requirement Analysis

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