Broccoli: Bug localization with the help of text search engines
This addresses the time-consuming bug localization process for software developers, but it is incremental as it extends existing methods with search engines.
The paper tackled bug localization in software development by investigating if text search engines can improve existing approaches, finding that including a search engine increased performance and exposed a flaw in benchmark strategies that underestimates prediction performance.
Bug localization is a tedious activity in the bug fixing process in which a software developer tries to locate bugs in the source code described in a bug report. Since this process is time-consuming and requires additional knowledge about the software project, information retrieval techniques can aid the bug localization process. In this paper, we investigate if normal text search engines can improve existing bug localization approaches. In a case study, we evaluate the performance of our search engine approach Broccoli against seven state-of-the-art bug localization algorithms on 82 open source projects in two data sets. Our results show that including a search engine can increase the performance of the bug localization and that it is a useful extension to existing approaches. As part of our analysis we also exposed a flaw in a commonly used benchmark strategy, i.e., that files of a single release are considered. To increase the number of detectable files, we mitigate this flaw by considering the state of the software repository at the time of the bug report. Our results show that using single releases may lead to an underestimation of the the prediction performance.