Evolutionary test data generation: A comparison of fitness functions.

Alison L. Watkins, Ellen M. Hufnagel

Research output: Contribution to journalArticlepeer-review

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.

Original languageAmerican English
JournalDefault journal
StatePublished - Jan 1 2006

Keywords

  • Automatic software test data generation
  • Genetic algorithms
  • Path testing
  • Adaptive search

Disciplines

  • Business

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