A COMPARATIVE STUDY OF THE PERFORMANCE OF IR MODELS ON DUPLICATE BUG DETECTION

Open source projects incorporate bug triagers to help with the task of bug report assignment to developers. One of the tasks of a triager is to identify whether an incoming bug report is a duplicate of a pre-existing report. In order to detect duplicate bug reports, a triager either relies on his memory and experience or on the search capabilities of the bug repository. Both these approaches can be time consuming for the triager and may also lead to the misidentification of duplicates. In the literature, several approaches to automate duplicate bug report detection have been proposed. However, there has not been an exhaustive comparison of the performance of different IR models, especially with topic-based ones such as LSI and LDA. In this paper, we compare the performance of the traditional vector space model (using different weighting schemes) with that of topic based models, leveraging heuristics that incorporate exception stack frames, surface features, summary and long description from the free-form text in the bug report. We perform experiments on subsets of bug reports from Eclipse and Firefox and achieve a recall rate of 60% and 58% respectively. We find that word-based models, in particular a Log-Entropy based weighting scheme, outperform topic based ones such as LSI, LDA and Random Projections. Our findings also suggests that for the problem of duplicate bug detection, it is important to consider a project’s domain and characteristics to devise a set of heuristics to achieve optimal results.