Evolutionary testing: a case study

Authors:
Stella Levin;Amiram Yehudai
Affiliations:
School of Computer Science, Tel-Aviv University;School of Computer Science, Tel-Aviv University
Venue:
HVC'06 Proceedings of the 2nd international Haifa verification conference on Hardware and software, verification and testing
Year:
2006

Citing 6
Cited 1

The chaining approach for software test data generation

ACM Transactions on Software Engineering and Methodology (TOSEM)
Genetic Algorithms in Search, Optimization and Machine Learning

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Generating Software Test Data by Evolution

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Automatic Test Data Generation For Structural Testing Of Embedded Software Systems By Evolutionary Testing

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Search-based software test data generation: a survey: Research Articles

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Experimental study on GA-based path-oriented test data generation using branch distance

IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application

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Abstract

The paper presents a case study of applying genetic algorithms (GAs) to the automatic test data generation problem. We present the basic techniques implemented in our prototype test generation system, whose goal is to get branch coverage of the program under testing. We used our tool to experiment with simple programs, programs that have been used by others for test strategies benchmarking and the UNIX utility uniq. The effectiveness of GA-based testing system is compared with a Random testing system. We found that for simple programs both testing systems work fine, but as the complexity of the program or the complexity of input domain grows, GA-based testing system significantly outperforms Random testing.