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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 24th Asia-Pacific Software Engineering Conference (APSEC 2017) |
| Editors | Jian Lv, He (Jason) Zhang, Mike Hinchey, Xiao Liu |
| Publisher | IEEE |
| Pages | 338-347 |
| ISBN (Electronic) | 978-1-5386-3681-7 |
| ISBN (Print) | 978-1-5386-3682-4 |
| DOIs | |
| Publication status | Published - Dec 2017 |
| Event | 24th Asia-Pacific Software Engineering Conference, APSEC 2017 - Nanjing, Jiangsu, China Duration: 4 Dec 2017 → 8 Dec 2017 http://www.apsec2017.org/ |
Publication series
| Name | Proceedings - Asia-Pacific Software Engineering Conference, APSEC |
|---|---|
| Volume | 2017-December |
| ISSN (Print) | 1530-1362 |
Conference
| Conference | 24th Asia-Pacific Software Engineering Conference, APSEC 2017 |
|---|---|
| Abbreviated title | APSEC 2017 |
| Place | China |
| City | Nanjing, Jiangsu |
| Period | 4/12/17 → 8/12/17 |
| Internet address |
Research Keywords
- bug localization
- convolutional neural network
- deep learning
- semantic information
- TF-IDF
- word2vec