Bug localization via searching crowd-contributed code

Abstract

Bug localization, i.e., locating bugs in code snippets, is a frequent task in software development. Although static bug-finding tools are available to reduce manual effort in bug localization, these tools typically detect bugs with known project-independent bug patterns. However, many bugs in real-world code snippets are project-specific. To address this issue, in this paper, we propose a novel approach for LOcating Bugs By Searching the most similar sample snippet (LOBBYS). LOBBYS detects bugs with the help of crowd-contributed correct code, which implement the function that buggy code is expected to implement. Given a buggy code snippet, LOBBYS takes two steps to locate the bug: (1) normalize the bug-gy snippet, and then search for the most similar sample snippet from the code base; (2) align the buggy code and sample code snip-pets, find the difference between the two code snippets, and generate a bug report based on the difference. To evaluate LOBBYS, we build one algorithm-oriented code base and select some buggy snippets from two real-world systems. The result shows that LOBBYS can effectively locate bugs for buggy snippets with high precision. Under the similarity of 50%, 70% and 90%, LOBBYS achieves bug-localization precision as 67%, 83%, and 92%.