Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
A functional approach to program testing and analysis
IEEE Transactions on Software Engineering
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Partition Testing Does Not Inspire Confidence (Program Testing)
IEEE Transactions on Software Engineering
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Constraint-Based Automatic Test Data Generation
IEEE Transactions on Software Engineering
Assertion-oriented automated test data generation
Proceedings of the 18th international conference on Software engineering
Predicate-based test generation for computer programs
ICSE '93 Proceedings of the 15th international conference on Software Engineering
Test templates: a specification-based testing framework
ICSE '93 Proceedings of the 15th international conference on Software Engineering
Software unit test coverage and adequacy
ACM Computing Surveys (CSUR)
The dynamic domain reduction procedure for test data generation
Software—Practice & Experience
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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IEEE Software
Automated verification and test case generation for input validation
Proceedings of the 2006 international workshop on Automation of software test
Improving evolutionary real-time testing
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 2007 international symposium on Software testing and analysis
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
The impact of input domain reduction on search-based test data generation
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Observations in using parallel and sequential evolutionary algorithms for automatic software testing
Computers and Operations Research
A tabu search algorithm for structural software testing
Computers and Operations Research
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
Optimisation of software testing using Genetic Algorithm
International Journal of Artificial Intelligence and Soft Computing
Comparing algorithms for search-based test data generation of matlab® simulink® models
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Information and Software Technology
Heuristic search-based approach for automated test data generation: a survey
International Journal of Bio-Inspired Computation
Dynamic stopping criteria for search-based test data generation for path testing
Information and Software Technology
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We present two stochastic search algorithms for generating test cases that execute specified paths in a program. The two algorithms are: a simulated annealing algorithm (SA), and a genetic algorithm (GA). These algorithms are based on an optimization formulation of the path testing problem which include both integer- and real-value test cases. We empirically compare the SA and GA algorithms with each other and with a hill-climbing algorithm, Korel's algorithm (KA), for integer-value-input subject programs and compare SA and GA with each other on real-value subject programs. Our empirical work uses several subject programs with a number of paths. The results show that: (a) SA and GA are superior to KA in the number of executed paths, (b) SA tends to perform slightly better than GA in terms of the number of executed paths, and (c) GA is faster than SA; however, KA, when it succeeds in finding the solution, is the fastest.