DocumentCode :
3116035
Title :
Breeding software test cases with genetic algorithms
Author :
Berndt, D. ; Fisher, J. ; Johnson, L. ; Pinglikar, J. ; Watkins, A.
Author_Institution :
Inf. Syst. & Decision Sci., South Florida Univ., Tampa, FL, USA
fYear :
2003
fDate :
6-9 Jan. 2003
Abstract :
Faulty software is usually costly and possibly life threatening as software becomes an increasingly critical component in a wide variety of systems. Thorough software testing by both developers and dedicated quality assurance staff is one way to uncover flaws. Automated test generation techniques can be used to augment the process, free of the cognitive biases that have been found in human testers. This paper focuses on breeding software test cases using genetic algorithms as part of a software testing cycle. An evolving fitness function that relies on a fossil record of organisms results in interesting search behaviours, based on the concepts of novelty, proximity, and severity. A case study that uses a simple, but widely studied program is used to illustrate the approach. Several visualization techniques are also introduced to analyze particular fossil records, as well as the overall search process.
Keywords :
genetic algorithms; program testing; program visualisation; quality assurance; software fault tolerance; automated test generation; evolving fitness function; faulty software; fossil record; genetic algorithms; quality assurance; search process; software test cases; software testing cycle; visualization techniques; Automatic testing; Computer aided software engineering; Costs; Data warehouses; Genetic algorithms; Management information systems; Software safety; Software testing; System testing; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on
Print_ISBN :
0-7695-1874-5
Type :
conf
DOI :
10.1109/HICSS.2003.1174917
Filename :
1174917
Link To Document :
بازگشت