On the Use of Stack Traces to Improve Text Retrieval-Based Bug Localization

Many bug localization techniques rely on Text Retrieval (TR) models. The most successful approaches have been proven to be the ones combining TR techniques with static analysis, dynamic analysis, and/or software repositories information. Dynamic software analysis and software repositories mining bring a significant overhead, as they require instrumenting and executing the software, and analyzing large amounts of data, respectively. We propose a new static technique, named Lobster (Locating Bugs using Stack Traces and text Retrieval), which is meant to improve TR-based bug localization without the overhead associated with dynamic analysis and repository mining. Specifically, we use the stack traces submitted in a bug report to compute the similarity between their code elements and the source code of a software system. We combine the stack trace based similarity and the textual similarity provided by TR techniques to retrieve code elements relevant to bug reports. We empirically evaluated Lobster using 155 bug reports containing stack traces from 14 open source software systems. We used Lucene, an optimized version of VSM, as baseline of comparison. The results show that, in average, Lobster improves or maintains the effectiveness of Lucene-based bug localization in 82% of the cases.