Improving Bug Localization with Character-level Convolutional Neural Network and Recurrent Neural Network

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

View graph of relations

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationPROCEEDINGS - 25th Asia-Pacific Software Engineering Conference - APSEC 2018
PublisherIEEE Computer Society
Pages703-704
ISBN (Print)978-1-7281-1970-0
Publication statusPublished - Dec 2018

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
Volume2018-December
ISSN (Print)1530-1362

Conference

Title25th Asia-Pacific Software Engineering Conference (APSEC 2018)
PlaceJapan
CityNara
Period4 - 7 December 2018

Abstract

Background: Automated bug localization in large amounts of source files for bug reports is a crucial task in software engineering. However, the different representations of bug reports and source files limited the accuracy of the existing bug localization techniques. Aims: We propose a novel deep learning-based model to improve the accuracy of bug localization for bug reports by expressing them in character and analyzing them with a language model. Method: The proposed model is composed of two main parts: character-level convolutional neural network (CNN) and recurrent neural network (RNN) language model. Both bug reports and source files are expressed in a character level and then input into a CNN, whose output is given to an RNN encoder-decoder architecture. Results: The results of preliminary experiments show that the proposed model achieves comparable or even higher accuracy than the existing machine translation-based bug localization technique. Conclusion: The proposed model is capable of automatically localizing buggy files for bug reports and achieves better accuracy by analyzing them in character level where both bug reports and source code can be expressed.

Research Area(s)

  • bug localization, convolutional neural network, recurrent neural network, deep learning

Citation Format(s)

Improving Bug Localization with Character-level Convolutional Neural Network and Recurrent Neural Network. / Xiao, Yan; Keung, Jacky.

PROCEEDINGS - 25th Asia-Pacific Software Engineering Conference - APSEC 2018. IEEE Computer Society, 2018. p. 703-704 8719518 (Proceedings - Asia-Pacific Software Engineering Conference, APSEC; Vol. 2018-December).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review