Skip Nav Destination
ASME Press Select Proceedings
International Conference on Computer Engineering and Technology, 3rd (ICCET 2011)Available to Purchase
ISBN:
9780791859735
No. of Pages:
970
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
82 Automatic Bug Assignment Using History of Packages Available to Purchase
By
Ramin Shokripour
,
Ramin Shokripour
Faculty of Computer Science & Information Technology,
University of Malaya
, Kuala Lumpur
, MALAYSIA
; [email protected]
Search for other works by this author on:
Zarinah Mohd Kasirun
Zarinah Mohd Kasirun
Faculty of Computer Science & Information,
Technology University of Malaya
, Kuala Lumpur
, MALAYSIA
; [email protected]
Search for other works by this author on:
Page Count:
8
-
Published:2011
Citation
Shokripour, R, Khansari, M, & Kasirun, ZM. "Automatic Bug Assignment Using History of Packages." International Conference on Computer Engineering and Technology, 3rd (ICCET 2011). Ed. Zhou, J. ASME Press, 2011.
Download citation file:
In the process of software debugging, a valid reported bug, after validation checking, should be assigned to a developer to fix. This paper presents an automatic approach to ease the bug assignment stage of the bug triage process. Our approach comprises two stages. In the first stage, the package to which the changed file to fix the bug belongs is predicted using the machine learning algorithm; and in the second stage, the most appropriate developer is recommended using the package activity histories of the predicted package. The aim of this paper is recommending the most appropriate developer to fix a new bug. We reach recall levels of 31% on the JDT component of Eclipse project.
ABSTRACT
KEYWORDS
1. Introduction
2. Background
3. Related Work
4. The Proposed Approach
5. Implementation and Evaluation
6. Conclusion and Future Work
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
User-Centric Process Descriptions
International Conference on Software Technology and Engineering, 3rd (ICSTE 2011)
Machine Learning to Judge Labor Relations' Harmoniousness Based on Decision Tree-Based Method
International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)
Regression of Representative Keys for Classification: A Simple Learning Approach
Intelligent Engineering Systems through Artificial Neural Networks
An Algorithm Implementation about SVR Based on Spider
International Symposium on Information Engineering and Electronic Commerce, 3rd (IEEC 2011)
Related Articles
A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data
J. Mech. Des (June,2016)
Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach
J. Energy Resour. Technol (February,2021)
Experimental Measurements Using Digital Image Correlation Methods: Brief Background and Perspective on Future Developments
J. Eng. Mater. Technol (January,2023)