Compositional Vector Space Models for Improved Bug Localization

Abstract : Software developers and maintainers often need to locate code units responsible for a particular bug. A numberof Information Retrieval (IR) techniques have been proposed to map natural language bug descriptions to the associated code units. The vector space model (VSM) with the standard tf-idf weighting scheme (VSM natural ), has been shown to outperform nine other state-of-the-art IR techniques. However, there are multiple VSM variants with different weighting schemes, and their relative performance differs for different software systems.Based on this observation, we propose to compose various VSM variants, modelling their composition as an optimization problem. We propose a genetic algorithm (GA) based approach to explore the space of possible compositions and output a heuristically near-optimal composite model. We have evaluated our approach against several baselines on thousands of bug reports from AspectJ, Eclipse, and SWT. On average, our approach (VSM composite ) improves hit at 5 (Hit@5), mean average precision (MAP), and mean reciprocal rank (MRR) scores of VSM natural by 18.4%, 20.6%, and 10.5% respectively. We also integrate our compositional model with AmaLgam, which is a state-of-art bug localization technique. The resultant model named AmaLgam composite on average can improve Hit@5, MAP, and MRR scores of AmaLgam by 8.0%, 14.4% and 6.5% respectively.
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Communication dans un congrès
30th International Conference on Software Maintenance and Evolution, Sep 2014, Victoria, Canada. pp.171-180
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https://hal.inria.fr/hal-01086084
Contributeur : Julia Lawall <>
Soumis le : vendredi 21 novembre 2014 - 18:15:45
Dernière modification le : vendredi 31 août 2018 - 09:25:54

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  • HAL Id : hal-01086084, version 1

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Shaowei Wang, David Lo, Julia Lawall. Compositional Vector Space Models for Improved Bug Localization. 30th International Conference on Software Maintenance and Evolution, Sep 2014, Victoria, Canada. pp.171-180. 〈hal-01086084〉

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