The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Automated test data generation for programs with procedures
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Fitness Function Design To Improve Evolutionary Structural Testing
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Instrumenting Programs With Flag Variables For Test Data Search By Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Improving Evolutionary Testing By Flag Removal
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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
Evolutionary testing of state-based programs
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Generating test data for distributed software using the chaining approach
Information and Software Technology
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Evolutionary testing of flag conditions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Proceedings of the 2007 international symposium on Software testing and analysis
Improving evolutionary class testing in the presence of non-public methods
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
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)
MC/DC automatic test input data generation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary testing of software with function-assigned flags
Journal of Systems and Software
Towards software test data generation using discrete quantum particle swarm optimization
Proceedings of the 3rd India software engineering conference
Mutation-driven generation of unit tests and oracles
Proceedings of the 19th international symposium on Software testing and analysis
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Fitness functions derived from certain types of white-box test goals can be inadequate for evolutionary software test data generation (Evolutionary Testing), due to a lack of search guidance to the required test data. Often this is because the fitness function does not take into account data dependencies within the program under test, and the fact that certain program statements may need to have been executed prior to the target structure in order for it to be feasible.This paper proposes a solution to this problem by hybridizing Evolutionary Testing with an extended Chaining Approach. The Chaining Approach is a method which identifies statements on which the target structure is data dependent, and incrementally develops chains of dependencies in an event sequence. By incorporating this facility into Evolutionary Testing, and by performing a test data search for each generated event sequence, the search can be directed into potentially promising, unexplored areas of the test object's input domain.Results presented in the paper show that test data can be found for a number of test goals with this hybrid approach that could not be found by using the original Evolutionary Testing approach alone. One such test goal is drawn from code found in the publicly available libpng library.