Large-Scale Patch Recommendation at Alibaba

Xindong Zhang, Chenguang Zhu, Yi Li, Jianmei Guo, Lihua Liu, Haobo Gu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present Precfix, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. Precfix collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22%. Precfix has been rolled out to Alibaba to support various critical businesses.

Original languageEnglish
Title of host publicationProceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering
Subtitle of host publicationCompanion, ICSE-Companion 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages252-253
Number of pages2
ISBN (Electronic)9781450371223
DOIs
StatePublished - Oct 2020
Externally publishedYes
Event42nd ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2020 - Seoul, Korea, Republic of
Duration: 27 Jun 202019 Jul 2020

Publication series

NameProceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion, ICSE-Companion 2020

Conference

Conference42nd ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2020
Country/TerritoryKorea, Republic of
CitySeoul
Period27/06/2019/07/20

Keywords

  • Defect detection
  • patch generation
  • patch recommendation

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