Static analyzers help find bugs early by warning about recurring bug categories. While fixing these bugs still remains a mostly manual task in practice, we observe that fixes for a specific bug category often are repetitive. This paper addresses the problem of automatically fixing instances of common bugs by learning from past fixes. We present Getafix, an approach that produces human-like fixes while being fast enough to suggest fixes in time proportional to the amount of time needed to obtain static analysis results in the first place.
Getafix is based on a novel hierarchical clustering algorithm that summarizes fix patterns into a hierarchy ranging from general to specific patterns. Instead of an expensive exploration of a potentially large space of candidate fixes, Getafix uses a simple yet effective ranking technique that uses the context of a code change to select the most appropriate fix for a given bug.
Our evaluation applies Getafix to 1,268 bug fixes for six bug categories reported by popular static analyzers for Java, including null dereferences, incorrect API calls, and misuses of particular language constructs. The approach predicts exactly the human-written fix as the top-most suggestion between 12% and 91% of the time, depending on the bug category. The top-5 suggestions contain fixes for 526 of the 1,268 bugs. Moreover, we report on deploying the approach within Facebook, where it contributes to the reliability of software used by billions of people. To the best of our knowledge, Getafix is the first industrially-deployed automated bug-fixing tool that learns fix patterns from past, human-written fixes to produce human-like fixes.
Fri 25 Oct
|11:00 - 11:22|
Shuai WangHong Kong University of Science and Technology, Chengyu ZhangEast China Normal University, Zhendong SuETH ZurichDOI
|11:22 - 11:45|
Rong PanUniversity of Texas at Austin, Qinheping HuUniversity of Wisconsin, Madison, Gaowei XuUniversity of Wisconsin Madison, Loris D'AntoniUniversity of Wisconsin MadisonDOI Pre-print
|11:45 - 12:07|
Johannes BaderFacebook, Andrew ScottFacebook, Michael PradelUniversity of Stuttgart, Satish ChandraFacebookDOI Pre-print
|12:07 - 12:30|
Bo ShenPeking University, Wei ZhangPeking University, Haiyan ZhaoPeking University, Guangtai LiangHuawei Technologies Co. Ltd, Zhi JinPeking University, Qianxiang WangHuawei Technologies Co. LtdDOI