Machine translation-based bug localization technique for bridging lexical gap
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 58-61 |
| Journal / Publication | Information and Software Technology |
| Volume | 99 |
| Online published | 6 Mar 2018 |
| Publication status | Published - Jul 2018 |
Link(s)
Abstract
Context: The challenge of locating bugs in mostly large-scale software systems has led to the development of bug localization techniques. However, the lexical mismatch between bug reports and source codes degrades the performances of existing information retrieval or machine learning-based approaches.
Objective: To bridge the lexical gap and improve the effectiveness of localizing buggy files by leveraging the extracted semantic information from bug reports and source code.
Method: We present BugTranslator, a novel deep learning-based machine translation technique composed of an attention-based recurrent neural network (RNN) Encoder-Decoder with long short-term memory cells. One RNN encodes bug reports into several context vectors that are decoded by another RNN into code tokens of buggy files. The technique studies and adopts the relevance between the extracted semantic information from bug reports and source files.
Results: The experimental results show that BugTranslator outperforms a current state-of-the-art word embedding technique on three open-source projects with higher MAP and MRR. The results show that BugTranslator can rank actual buggy files at the second or third places on average.
Conclusion: BugTranslator distinguishes bug reports and source code into different symbolic classes and then extracts deep semantic similarity and relevance between bug reports and the corresponding buggy files to bridge the lexical gap at its source, thereby further improving the performance of bug localization.
Objective: To bridge the lexical gap and improve the effectiveness of localizing buggy files by leveraging the extracted semantic information from bug reports and source code.
Method: We present BugTranslator, a novel deep learning-based machine translation technique composed of an attention-based recurrent neural network (RNN) Encoder-Decoder with long short-term memory cells. One RNN encodes bug reports into several context vectors that are decoded by another RNN into code tokens of buggy files. The technique studies and adopts the relevance between the extracted semantic information from bug reports and source files.
Results: The experimental results show that BugTranslator outperforms a current state-of-the-art word embedding technique on three open-source projects with higher MAP and MRR. The results show that BugTranslator can rank actual buggy files at the second or third places on average.
Conclusion: BugTranslator distinguishes bug reports and source code into different symbolic classes and then extracts deep semantic similarity and relevance between bug reports and the corresponding buggy files to bridge the lexical gap at its source, thereby further improving the performance of bug localization.
Research Area(s)
- Bug localization, Deep learning, Lexical mismatch, Machine translation
Citation Format(s)
Machine translation-based bug localization technique for bridging lexical gap. / Xiao, Yan; Keung, Jacky; Bennin, Kwabena E. et al.
In: Information and Software Technology, Vol. 99, 07.2018, p. 58-61.
In: Information and Software Technology, Vol. 99, 07.2018, p. 58-61.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
