#Mucommander v0.9.0 download code#
More specifically, we propose a streaming bug localization technique, based on an ensemble of online topic models, that is able to adapt to both specific (with explicit code mentions) and more abstract bug reports. In this paper, we propose a technique for online bug localization, which enables rapidly updatable bug localization models.
#Mucommander v0.9.0 download software#
However, with modern software development trending towards increased speed of software change and continuous delivery to the user, the current generation of bug localization techniques, which cannot quickly adapt to the latest version of the software, is becoming inadequate. To date, researchers have primarily addressed this problem by applying different information retrieval techniques that leverage similarities between a given bug report and source code. Our findings indicate that our approach is a suitable alternative to the standard approach, providing comparable accuracy while eliminating retraining costs.īug localization aims to reduce debugging time by recommending program elements that are relevant for a specific bug report. Moreover, our results demonstrate that context and messages are key to the accuracy of changeset-based models and that the time invariance assumption has a statistically significant effect on evaluation results, providing overly-optimistic results. Our results demonstrate that our approach is as accurate as the standard approach for projects with most changes localized to a subset of the code, but less accurate when changes are highly distributed throughout the code. In this paper, we expand our work by investigating: a second task (developer identification), the effects of including different changeset parts in the model, the repository characteristics that affect the accuracy of our approach, and the effects of the time invariance assumption on evaluation results.
To address this, we previously introduced an approach that trains a model on documents representing changesets from a repository and demonstrated its feasibility for feature location. However, code changes lead to model obsolescence and to the need to retrain the model from the latest snapshot. The standard approach to applying text retrieval models to code repositories is to train models on documents representing program elements.
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