Search: L773:1865 1356 OR L773:1865 1348 OR L773:9783030942373 >
Automated Code Revi...
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Ochodek, M.Politechnika Poznanska,Poznan University of Technology
(author)
Automated Code Review Comment Classification to Improve Modern Code Reviews
- Article/chapterEnglish2022
Publisher, publication year, extent ...
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2022-04-12
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Cham :Springer International Publishing,2022
Numbers
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LIBRIS-ID:oai:research.chalmers.se:3b5b0577-3aef-4181-aee2-33bcc6b6efde
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https://research.chalmers.se/publication/530252URI
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https://doi.org/10.1007/978-3-031-04115-0_3DOI
Supplementary language notes
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Language:English
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Summary in:English
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Classification
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Subject category:kon swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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Modern Code Reviews (MCRs) are a widely-used quality assurance mechanism in continuous integration and deployment. Unfortunately, in medium and large projects, the number of changes that need to be integrated, and consequently the number of comments triggered during MCRs could be overwhelming. Therefore, there is a need for quickly recognizing which comments are concerning issues that need prompt attention to guide the focus of the code authors, reviewers, and quality managers. The goal of this study is to design a method for automated classification of review comments to identify the needed change faster and with higher accuracy. We conduct a Design Science Research study on three open-source systems. We designed a method (CommentBERT) for automated classification of the code-review comments based on the BERT (Bidirectional Encoder Representations from Transformers) language model and a new taxonomy of comments. When applied to 2,672 comments from Wireshark, The Mono Framework, and Open Network Automation Platform (ONAP) projects, the method achieved accuracy, measured using Matthews Correlation Coefficient, of 0.46–0.82 (Wireshark), 0.12–0.8 (ONAP), and 0.48–0.85 (Mono). Based on the results, we conclude that the proposed method seems promising and could be potentially used to build machine-learning-based tools to support MCRs as long as there is a sufficient number of historical code-review comments to train the model.
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Added entries (persons, corporate bodies, meetings, titles ...)
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Staron, Miroslaw,1977Göteborgs universitet,University of Gothenburg(Swepub:cth)miroslaw
(author)
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Meding, Wilhelm,1970Telefonaktiebolaget L M Ericsson,Ericsson
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Söder, Ola
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Politechnika PoznanskaGöteborgs universitet
(creator_code:org_t)
Related titles
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In:Lecture Notes in Business Information ProcessingCham : Springer International Publishing439 LNBIP, s. 23-401865-13561865-1348
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