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Test-data generation using genetic algorithms

✍ Scribed by Roy P. Pargas; Mary Jean Harrold; Robert R. Peck

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 214 KB
Volume: 9
Category: Article
ISSN: 0960-0833
DOI: 10.1002/(sici)1099-1689(199912)9:4<263::aid-stvr190>3.0.co;2-y

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✦ Synopsis

This paper presents a technique that uses a genetic algorithm for automatic test-data generation.

A genetic algorithm is a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem. In the test-data generation application, the solution sought by the genetic algorithm is test data that causes execution of a given statement, branch, path, or definition-use pair in the program under test. The test-data-generation technique was implemented in a tool called TGen, in which parallel processing was used to improve the performance of the search. To experiment with TGen, a random test-data generator called Random was also implemented. Both TGen and Random were used to experiment with the generation of test-data for statement and branch coverage of six programs.

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