Crowd debugging

Abstract

Research shows that, in general, many people turn to QA sites to solicit answers to their problems. We observe in Stack Overflow a huge number of recurring questions, 1,632,590, despite mechanisms having been put into place to prevent these recurring questions. Recurring questions imply developers are facing similar issues in their source code. However, limitations exist in the QA sites. Developers need to visit them frequently and/or should be familiar with all the content to take advantage of the crowd's knowledge. Due to the large and rapid growth of QA data, it is difficult, if not impossible for developers to catch up. To address these limitations, we propose mining the QA site, Stack Overflow, to leverage the huge mass of crowd knowledge to help developers debug their code. Our approach reveals 189 warnings and 171 (90.5%) of them are confirmed by developers from eight high-quality and well-maintained projects. Developers appreciate these findings because the crowd provides solutions and comprehensive explanations to the issues. We compared the confirmed bugs with three popular static analysis tools (FindBugs, JLint and PMD). Of the 171 bugs identified by our approach, only FindBugs detected six of them whereas JLint and PMD detected none.

BibTeX

@inproceedings{Chen-Kim:FSE15,
  author    = {Fuxiang Chen and
               Sunghun Kim},
  title     = {Crowd debugging},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {320--332},
  publisher = {{ACM}},
  year      = {2015},
}

Crowd debugging

Abstract

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