Large-Scale Patch Recommendation at Alibaba

被引：0

作者：

Zhang, Xindong ^{[1
]}

Zhu, Chenguang ^{[2
]}

Li, Yi ^{[3
]}

Guo, Jianmei ^{[1
]}

Liu, Lihua ^{[1
]}

Gu, Haobo ^{[1
]}

机构：

[1] Alibaba Grp, Hangzhou, Peoples R China

[2] Univ Texas Austin, Austin, TX 78712 USA

[3] Nanyang Technol Univ, Singapore, Singapore

来源：

2020 ACM/IEEE 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2020) | 2020年

关键词：

Defect detection; patch generation; patch recommendation;

D O I：

10.1145/3377812.3390902

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：252 / 253

页数：2

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