Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Using genetic algorithms to generate test plans for functionality testing
Proceedings of the 44th annual Southeast regional conference
Code-coverage guided prioritized test generation
Information and Software Technology
GA-based multiple paths test data generator
Computers and Operations Research
A probabilistic alternative to regression suites
Theoretical Computer Science
Use of genetic algorithm in generation of feasible test data
ACM SIGSOFT Software Engineering Notes
Optimisation of software testing using Genetic Algorithm
International Journal of Artificial Intelligence and Soft Computing
Evolutionary software engineering, a review
Applied Soft Computing
Optimization of software testing using genetic algorithms
MACMESE'09 Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering
Comparing algorithms for search-based test data generation of matlab® simulink® models
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Opaque predicates detection by abstract interpretation
AMAST'06 Proceedings of the 11th international conference on Algebraic Methodology and Software Technology
Heuristic search-based approach for automated test data generation: a survey
International Journal of Bio-Inspired Computation
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In software testing, it is often desirable to find test inputs that exercise specific program features. To find these inputs by hand is extremely time-consuming, especially when the software is complex. Therefore, numerous attempts have been made to automate the process. Random test data generation consists of generating test inputs at random, in the hope that they will exercise the desired software features. Often, the desired inputs must satisfy complex constraints, and this makes a random approach seem unlikely to succeed. In contrast, combinatorial optimization techniques, such as those using genetic algorithms, are meant to solve difficult problems involving the simultaneous satisfaction of many constraints. In this paper, we discuss experiments with a test generation problem that is harder than the ones discussed in earlier literature-we use a larger program and more complex test adequacy criteria. We find a widening gap between a technique based on genetic algorithms and those based on random test generation.