Towards A Novel Approach for Defect Localization Based on Part-of-Speech and Invocation

Tong, Yanxiang; Zhou, Yu; Fang, Lisheng; Chen, Taolue

doi:10.1145/2875913.2875919

Cited by 1 publication

(3 citation statements)

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“…Moreover, due to the characteristic of the source code, we cannot say that the invocation corpus contains all the invocation files of a particular file. Compared to the simple string-based searching method used in [27], the invocation corpus can avoid re-calculating each time.…”

Section: Threats To Validitymentioning

confidence: 99%

“…This paper is based on our previous work [27], but with significant extensions. We doubled the size of our empirically studied cases-from three to six open source projects-to minimize the external threats to validity.…”

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confidence: 99%

“…Moreover, we optimize the process of rendering invocation relationship. In our previous paper [27], we simply used the string-based match to find the invocation files of the highest score file which is easy to implement. However, the performance of this method (implemented in module 2 of our approach; cf.…”

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confidence: 99%

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Augmenting Bug Localization with Part-of-Speech and Invocation

Zhou

Tong

Chen

et al. 2017

Int. J. Soft. Eng. Knowl. Eng.

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Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of our approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e., ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

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Section: Threats To Validitymentioning

confidence: 99%

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confidence: 99%