Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Automated test data generation using an iterative relaxation method
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Art of Software Testing
What Is Software Testing? And Why Is It So Hard?
IEEE Software
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Suitability of Evolutionary Algorithms for Evolutionary Testing
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Fitness Function Design To Improve Evolutionary Structural Testing
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using genetic algorithms for test case generation in path testing
ATS '00 Proceedings of the 9th Asian Test Symposium
Genetic algorithms for dynamic test data generation
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
Identification of Potentially Infeasible Program Paths by Monitoring the Search for Test Data
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
Breeding Software Test Cases with Genetic Algorithms
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Investigating the performance of genetic algorithm-based software test case generation
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Automated test data generation using a scatter search approach
Information and Software Technology
Search-based multi-paths test data generation for structure-oriented testing
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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
Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
Test data generation approach for basis path coverage
ACM SIGSOFT Software Engineering Notes
Grouping target paths for evolutionary generation of test data in parallel
Journal of Systems and Software
Evolutionary algorithms for the multi-objective test data generation problem
Software—Practice & Experience
Search based software test data generation for structural testing: a perspective
ACM SIGSOFT Software Engineering Notes
Dynamic stopping criteria for search-based test data generation for path testing
Information and Software Technology
Generating test data for both path coverage and fault detection using genetic algorithms
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Developers have learned over time that software testing costs a considerable amount of a software project budget. Hence, software quality managers have been looking for solutions to reduce testing costs and time. Considering path coverage as the test adequacy criterion, we propose using genetic algorithms (GA) for automating the generation of test data for white-box testing. There are evidences that GA has been already successful in generating test data. However, existing GA-based test data generators suffer from some problems. This paper presents our approach to overcome one of these problems; that is the inefficiency in covering multiple target paths. We have designed a GA-based test data generator that is, in one run, able to synthesize multiple test data to cover multiple target paths. Moreover, we have implemented a set of variations of the generator. Experimental results show that our test data generator is more efficient and more effective than others.