Investigating the performance of genetic algorithm-based software test case generation

Authors:
Donald J. Berndt;Alison Watkins
Affiliations:
National Institute for Systems Test and Productivity College of Business Administration, University of South Florida;National Institute for Systems Test and Productivity College of Business Administration, University of South Florida
Venue:
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Year:
2004

Citing 1
Cited 6

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

Using genetic algorithms to generate test plans for functionality testing

Proceedings of the 44th annual Southeast regional conference
GA-based multiple paths test data generator

Computers and Operations Research
Deriving evaluation metrics for applicability of genetic algorithms to optimization problems

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Automatic, evolutionary test data generation for dynamic software testing

Journal of Systems and Software
Optimisation of software testing using Genetic Algorithm

International Journal of Artificial Intelligence and Soft Computing
Optimization of software testing using genetic algorithms

MACMESE'09 Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Highly complex and interconnected systems may suffer from intermittent or transient failures that are particularly difficult to diagnose. This research focuses on the use of genetic algorithms for automatically generating large volumes of software test cases. In particular, the paper explores two fundamental strategies for improving the performance of genetic algorithm test case breeding for high volume testing. The first strategy seeks to avoid evaluating test cases against the real target system by using oracles or models. The second strategy involves improving the more costly components of genetic algorithms, such as fitness function calculations. Together, the various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.