원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.4 No.1
2010.01
pp.43-52
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Machine-learning approaches have been widely used for fault-proneness detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spam-filtering technique. To treat our approach as the conventional faultprone approaches, we summarize the output of spam-filtering based approach as a metric. In this paper, we show the effectiveness of our new metric comparing the conventional software metrics using two open source projects.
목차
Abstract
1. Introduction
2. Objective
3. Metrics suite and prediction models
3.1. History metrics
3.2. Complexity metrics
3.3. Text filtering metrics
3.4. Fault-prone module prediction models
4. Experiment
4.1. Collection of faulty modules
4.2. Evaluation measures
4.2. Evaluation measures
4.3. Results of experiment
4.4. Discussion
5. Threats to validity
6. Related work
7. Conclusion
References
1. Introduction
2. Objective
3. Metrics suite and prediction models
3.1. History metrics
3.2. Complexity metrics
3.3. Text filtering metrics
3.4. Fault-prone module prediction models
4. Experiment
4.1. Collection of faulty modules
4.2. Evaluation measures
4.2. Evaluation measures
4.3. Results of experiment
4.4. Discussion
5. Threats to validity
6. Related work
7. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보