|
|
|
Series in Machine Perception and Artificial Intelligence - Vol. 56
ARTIFICIAL INTELLIGENCE METHODS IN SOFTWARE TESTING
edited by Mark Last (Ben-Gurion University of the Negev, Israel), Abraham Kandel (Tel-Aviv University, Israel & University of South Florida, USA) & Horst Bunke (University of Bern, Switzerland)
An inadequate infrastructure for software testing is causing major losses to the world economy. The characteristics of software quality problems are quite similar to other tasks successfully tackled by artificial intelligence techniques. The aims of this book are to present state-of-the-art applications of artificial intelligence and data mining methods to quality assurance of complex software systems, and to encourage further research in this important and challenging area.
Contents:
- Fuzzy Cause–Effect Models of Software Testing (W Pedrycz
& G Vukovich)
- Black-Box Testing with Info-Fuzzy Networks (M Last & M Friedman)
- Automated GUI Regression Testing Using AI Planning (A M Memon)
- Test Set Generation and Reduction with Artificial Neural Networks (P Saraph et al.)
- Three-Group Software Quality Classification Modeling Using an Automated Reasoning Approach (T M Khoshgoftaar & N Seliya)
- Data Mining with Resampling in Software Metrics Databases (S Dick & A Kandel)
Readership: Students, researchers and professionals in computer
science, information systems, software testing and data mining.
| 220pp |
Pub. date: Jun 2004 |
|
|
