Publication Type

Conference Proceeding Article

Publication Date

10-2014

Abstract

Software developers and maintainers often need to locate code units responsible for a particular bug. A number of 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 (VSMnatural), 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 (VSMcomposite ) improves hit at 5 (Hit@5), mean average precision (MAP), and mean reciprocal rank (MRR) scores of VSMnatural by 18.4%, 20.6%, and 10.5% respectively. We also integrate our compositional model with AmaLgam, which is a stateof-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.

Keywords

genetic algorithms, information retrieval, natural language processing, program debugging, software maintenance, vectors

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE International Conference on Software Maintenance and Evolution (ICSME): Proceedings: September 29 - October 3, 2014, Victoria, Canada

First Page

171

Last Page

180

Identifier

10.1109/ICSME.2014.39

Publisher

IEEE

City or Country

Piscataway, NJ

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/ICSME.2014.39

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