Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Programming: the derivation of algorithms
Programming: the derivation of algorithms
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Automated test-data generation for exception conditions
Software—Practice & Experience
Symbolic execution and program testing
Communications of the ACM
The Science of Programming
A Discipline of Programming
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
A new approach to program testing
Proceedings of the international conference on Reliable software
Evolutionary testing in the presence of loop-assigned flags: a testability transformation approach
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
The state problem for test generation in Simulink
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Software Testing, Verification & Reliability - UKTest 2005: The Third U.K. Workshop on Software Testing Research
Empirical Software Engineering
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Pareto efficient multi-objective test case selection
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
Automatic Generation of Floating-Point Test Data
IEEE Transactions on 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
GA-based multiple paths test data generator
Computers and Operations Research
Cellular Genetic Algorithms
Automated test data generation using a scatter search approach
Information and Software Technology
Empirical evaluation of a nesting testability transformation for evolutionary testing
ACM Transactions on Software Engineering and Methodology (TOSEM)
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
ICSTW '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification, and Validation Workshops
Evolutionary repair of faulty software
Applied Soft Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Cellular automata based test data generation
ACM SIGSOFT Software Engineering Notes
Estimating software testing complexity
Information and Software Technology
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Automatic test data generation is a very popular domain in the field of search-based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense by considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we did in this paper. We mainly compared two approaches to deal with the multi-objective test data generation problem: a direct multi-objective approach and a combination of a mono-objective algorithm together with multi-objective test case selection optimization. Concretely, in this work, we used four state-of-the-art multi-objective algorithms and two mono-objective evolutionary algorithms followed by a multi-objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed of 800 Java programs created through a program generator. The second benchmark is composed of 13 real programs extracted from the literature. In the direct multi-objective approach, the results indicate that the oracle cost can be properly optimized; however, the full branch coverage of the system poses a great challenge. Regarding the mono-objective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective in maximizing the branch coverage. Copyright © 2011 John Wiley & Sons, Ltd.