Evolutionary test data generation: a comparison of fitness functions: Research Articles

Authors:
Alison Watkins;Ellen M. Hufnagel
Affiliations:
Information Systems and Decision Sciences, College of Business, University of South Florida St Petersburg, 140 Seventh Avenue South, St Petersburg, FL 33701, U.S.A.;Information Systems and Decision Sciences, College of Business, University of South Florida St Petersburg, 140 Seventh Avenue South, St Petersburg, FL 33701, U.S.A.
Venue:
Software—Practice & Experience
Year:
2006

Citing 0
Cited 9

Heuristics-based infeasible path detection for dynamic test data generation

Information and Software Technology
Automated test data generation using a scatter search approach

Information and Software Technology
On the value of combining feature subset selection with genetic algorithms: faster learning of coverage models

PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Search-based multi-paths test data generation for structure-oriented testing

Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Search-based software testing and test data generation for a dynamic programming language

Proceedings of the 13th annual conference on Genetic and evolutionary computation
PSO based pseudo dynamic method for automated test case generation using interpreter

ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Evolutionary generation of test data for many paths coverage based on grouping

Journal of Systems and Software
Controversy Corner: Search Based Software Engineering: Review and analysis of the field in Brazil

Journal of Systems and Software
Cellular automata based test data generation

ACM SIGSOFT Software Engineering Notes

Quantified Score

Hi-index	0.00

Visualization

Abstract

Previous research using genetic algorithms to automate the generation of data for path testing has utilized several different fitness functions, assessing their usefulness by comparing them to random generation. This paper describes two sets of experiments that assess the performance of several fitness functions, relative to one another and to random generation. The results demonstrate that some fitness functions provide better results than others, generating fewer test cases to exercise a given program path. In these studies, the branch predicate and inverse path probability approaches were the best performers, suggesting that a two-step process combining these two methods may be the most efficient and effective approach to path testing. Copyright © 2005 John Wiley & Sons, Ltd.