Improving Bug Localization with an Enhanced Convolutional Neural Network

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 24th Asia-Pacific Software Engineering Conference (APSEC 2017)
EditorsJian Lv, He (Jason) Zhang, Mike Hinchey, Xiao Liu
PublisherIEEE
Pages338-347
ISBN (Electronic)978-1-5386-3681-7
ISBN (Print)978-1-5386-3682-4
Publication statusPublished - Dec 2017

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume2017-December
ISSN (Print)1530-1362

Conference

Title24th Asia-Pacific Software Engineering Conference, APSEC 2017
PlaceChina
CityNanjing, Jiangsu
Period4 - 8 December 2017

Abstract

Background: Localizing buggy files automatically speeds up the process of bug fixing so as to improve the efficiency and productivity of software quality teams. There are other useful semantic information available in bug reports and source code, but are mostly underutilized by existing bug localization approaches.

Aims: We propose DeepLocator, a novel deep learning based model to improve the performance of bug localization by making full use of semantic information.

Method: DeepLocator is composed of an enhanced CNN (Convolutional Neural Network) proposed in this study considering bug-fixing experience, together with a new rTF-IDuF method and pretrained word2vec technique. DeepLocator is then evaluated on over 18,500 bug reports extracted from AspectJ, Eclipse, JDT, SWT and Tomcat projects.

Results: The experimental results show that DeepLocator achieves 9.77% to 26.65% higher Fmeasure than the conventional CNN and 3.8% higher MAP than a state-of-the-art method HyLoc using less computation time.

Conclusion: DeepLocator is capable of automatically connecting bug reports to the corresponding buggy files and successfully achieves better performance based on a deep understanding of semantics in bug reports and source code.

Research Area(s)

  • bug localization, convolutional neural network, deep learning, semantic information, TF-IDF, word2vec

Citation Format(s)

Improving Bug Localization with an Enhanced Convolutional Neural Network. / Xiao, Yan; Keung, Jacky; Mi, Qing; Bennin, Kwabena E.

Proceedings - 24th Asia-Pacific Software Engineering Conference (APSEC 2017). ed. / Jian Lv; He (Jason) Zhang; Mike Hinchey; Xiao Liu. IEEE, 2017. p. 338-347 (Proceedings - Asia-Pacific Software Engineering Conference, APSEC; Vol. 2017-December).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)