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Mining Source Code Improvement Patterns from Similar Code Review Works

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dc.contributor.author Ueda, Yuki
dc.contributor.author Ishio, Takashi
dc.contributor.author Ihara, Akinori
dc.contributor.author Matsumoto, Kenichi
dc.date.accessioned 2019-04-02T00:43:53Z
dc.date.available 2019-04-02T00:43:53Z
dc.date.issued 2019
dc.identifier.isbn 9781728118055
dc.identifier.issn 2572-6587
dc.identifier.uri http://hdl.handle.net/10061/13125
dc.description IWSC 2019 : 2019 IEEE 13th International Workshop on Software Clones, 24-24 Feb. 2019, Hangzhou, China
dc.description.abstract Code review is key to ensuring the absence of potential issues in source code. Code reviewers spend a large amount of time to manually check submitted patches based on their knowledge. Since a number of patches sometimes have similar potential issues, code reviewers need to suggest similar source code changes to patch authors. If patch authors notice similar code improvement patterns by themselves before submitting to code review, reviewers’ cost would be reduced. In order to detect similar code changes patterns, this study employs a sequential pattern mining to detect source code improvement patterns that frequently appear in code review history. In a case study using a code review dataset of the OpenStack project, we found that the detected patterns by our proposed approach included effective examples to improve patches without reviewers’ manual check. We also found that the patterns have been changed in time series; our pattern mining approach timely achieves to track the effective code improvement patterns.
dc.language.iso en
dc.publisher IEEE
dc.rights c Copyright IEEE 2019
dc.subject Encoding
dc.subject Guidelines
dc.subject Software
dc.subject Training
dc.subject History
dc.subject Manuals
dc.subject Time series analysis
dc.subject code review
dc.subject source code changes
dc.subject sequential pattern mining
dc.title Mining Source Code Improvement Patterns from Similar Code Review Works
dc.type.nii Conference Paper
dc.identifier.fulltexturl https://doi.org/10.1109/IWSC.2019.8665852
dc.textversion author
dc.identifier.spage 13
dc.identifier.epage 19
dc.relation.doi 10.1109/IWSC.2019.8665852
dc.identifier.NAIST-ID 74653577
dc.identifier.NAIST-ID 73292310

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