원문정보
보안공학연구지원센터(IJSEIA)
International Journal of Software Engineering and Its Applications
Vol.6 No.4
2012.10
pp.1-16
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
A challenging part of software testing entails the generation of test cases. A good test case should have the quality to cover more features of test objective. The techniques for automatic generation of test cases try to efficiently find a small set of cases that allow an adequacy criterion to be fulfilled, thus reducing the cost of software testing and resulting in more efficient testing of software products. In this paper we introduce an all-around classification framework for automatic test case generation approaches in terms of test type and Algorithm, and represent some test case evaluation approaches. Finally we illustrate a comparison between different existing techniques.
목차
Abstract
1. Introduction
2. Overview
3. Automatic Test Case Generation
4. Proposed Classification Framework in Terms of Test Type
5. Proposed Classification Framework in Terms of Algorithm
5.1. Random-based Automatic Test Case Generation
5.2. Search-based Automatic Test Case Generation
5.3. Data Mining-based Automatic Test Case Generation
6. Classification of Test Case Generation Evaluation Approaches
6.1. Number of Faults Detected (mutation testing):
6.2. Coverage Criteria:
6.3. Input/output Analysis
6.4. Data Mining Approaches:
8. Discussions
7. Conclusion
Acknowledgements
References
1. Introduction
2. Overview
3. Automatic Test Case Generation
4. Proposed Classification Framework in Terms of Test Type
5. Proposed Classification Framework in Terms of Algorithm
5.1. Random-based Automatic Test Case Generation
5.2. Search-based Automatic Test Case Generation
5.3. Data Mining-based Automatic Test Case Generation
6. Classification of Test Case Generation Evaluation Approaches
6.1. Number of Faults Detected (mutation testing):
6.2. Coverage Criteria:
6.3. Input/output Analysis
6.4. Data Mining Approaches:
8. Discussions
7. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보