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Classification of Bug Injected and Fixed Changes Using a Text Discriminator

Classification of Bug Injected and Fixed Changes Using a Text Discriminator

Akihisa Yamada, Osamu Mizuno
Copyright: © 2015 |Volume: 3 |Issue: 1 |Pages: 13
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781466680593|DOI: 10.4018/ijsi.2015010104
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MLA

Yamada, Akihisa, and Osamu Mizuno. "Classification of Bug Injected and Fixed Changes Using a Text Discriminator." IJSI vol.3, no.1 2015: pp.50-62. http://doi.org/10.4018/ijsi.2015010104

APA

Yamada, A. & Mizuno, O. (2015). Classification of Bug Injected and Fixed Changes Using a Text Discriminator. International Journal of Software Innovation (IJSI), 3(1), 50-62. http://doi.org/10.4018/ijsi.2015010104

Chicago

Yamada, Akihisa, and Osamu Mizuno. "Classification of Bug Injected and Fixed Changes Using a Text Discriminator," International Journal of Software Innovation (IJSI) 3, no.1: 50-62. http://doi.org/10.4018/ijsi.2015010104

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Abstract

Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, the authors proposed a technique using a text filtering technique. They assume that bugs relate to words and context that are contained in a software module. The technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing probability over a target module is calculated, and it judges whether the given module is a fault-prone module. The predictive granularity of this technique is a module. In this study, the authors aimed at prediction with the finer granularity of the portion that induces a bug. Specifically, they tried to predict bug-inducing changes by using source code differences of bug inducing changes and previous changes and a text filtering technique. Similarly, the authors tried to predict bug fixing by using source code differences of bug fixing changes and previous changes and a text filtering technique. To show the effectiveness of the approach, the authors conducted two experiments and compared their approach with fault-prone filtering by applying it to two open source projects, and obtained higher accuracy.

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