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Test‐data generation using genetic algorithms
Author(s) -
Pargas Roy P.,
Harrold Mary Jean,
Peck Robert R.
Publication year - 1999
Publication title -
software testing, verification and reliability
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 49
eISSN - 1099-1689
pISSN - 0960-0833
DOI - 10.1002/(sici)1099-1689(199912)9:4<263::aid-stvr190>3.0.co;2-y
Subject(s) - computer science , algorithm , genetic algorithm , statement (logic) , test data , generator (circuit theory) , heuristic , path (computing) , test (biology) , random number generation , automatic test pattern generation , data mining , machine learning , artificial intelligence , programming language , engineering , power (physics) , biology , electronic circuit , electrical engineering , paleontology , physics , quantum mechanics , political science , law
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. Copyright © 1999 John Wiley & Sons, Ltd.