Automated Software Test Data Generation
IEEE Transactions on Software Engineering
The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Effect of test set minimization on fault detection effectiveness
Software—Practice & Experience
Testing object-oriented systems: models, patterns, and tools
Testing object-oriented systems: models, patterns, and tools
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
A Cost-Benefit Stopping Criterion for Statistical Testing
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 9 - Volume 9
Evolutionary testing of classes
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
An experimental mutation system for Java
ACM SIGSOFT Software Engineering Notes
Rostra: A Framework for Detecting Redundant Object-Oriented Unit Tests
Proceedings of the 19th IEEE international conference on Automated software engineering
Predicting the Location and Number of Faults in Large Software Systems
IEEE Transactions on Software Engineering
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
The species per path approach to SearchBased test data generation
Proceedings of the 2006 international symposium on Software testing and analysis
On the Automated Generation of Program Test Data
IEEE Transactions on Software Engineering
A hybrid optimization technique coupling an evolutionary and a local search algorithm
Journal of Computational and Applied Mathematics
The state problem for evolutionary testing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Foundations of Software Testing
Foundations of Software Testing
Effective black-box testing with genetic algorithms
HVC'05 Proceedings of the First Haifa international conference on Hardware and Software Verification and Testing
Test sequence optimisation: an intelligent approach via cuckoo search
International Journal of Bio-Inspired Computation
Critical components testing using hybrid genetic algorithm
ACM SIGSOFT Software Engineering Notes
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Software development organizations spend considerable portion of their budget and time in testing related activities. The effectiveness of the verification and validation process depends upon the number of errors found and rectified before releasing the software to the customer side. This in turn depends upon the quality of test cases generated. The solution is to choose the most important and effective test cases and removing the redundant and unnecessary ones; which in turn leads to test case optimization. To achieve test case optimization, this paper proposed a heuristics guided population based search approach namely Hybrid Genetic Algorithm (HGA) which combines the features of Genetic Algorithm (GA) and Local Search (LS) techniques to reduce the number of test cases by improving the quality of test cases during the solution generation process. Also, to evaluate the performance of the proposed approach, a comparative study is conducted with Genetic Algorithm and Bacteriologic Algorithm (BA) and concluded that, the proposed HGA based approach produces better results.