Abstract
In the software testing process, selecting the test cases and verifying their results requires a lot of subjective decisions and human intervention. For a program having a large number of inputs, the number of corresponding combinatorial black-box test cases is huge. A method needs to be established in order to limit the number of test cases and to choose the most important ones. In this research effort we present a novel methodology for identifying important test cases automatically. These test cases involve input attributes which contribute to the value of an output and hence are significant. The reduction in the number of test cases is attributed to identifying input-output relationships. A ranked list of features and equivalence classes for input attributes of a given code are the main outcomes of this methodology. Reducing the number of test cases results directly in the saving of software testing resources.
| Original language | English |
|---|---|
| Pages (from-to) | 768-773 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 1 |
| State | Published - 24 Nov 2003 |
| Event | System Security and Assurance - Washington, DC, United States Duration: 5 Oct 2003 → 8 Oct 2003 |
Keywords
- Artificial neural networks
- Feature ranking
- Input-output analysis
- Rule-extraction
- Software testing
- Test cases
ASJC Scopus subject areas
- Control and Systems Engineering
- Hardware and Architecture