Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Fitness Function Design To Improve Evolutionary Structural Testing
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
An Automated Framework for Structural Test-Data Generation
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
VADA: A Transformation-Based System for Variable Dependence Analysis
SCAM '02 Proceedings of the Second IEEE International Workshop on Source Code Analysis and Manipulation
Testing the Results of Static Worst-Case Execution-Time Analysis
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
A Post-Placement Side-Effect Removal Algorithm
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
An extension to the cyclomatic measure of program complexity
ACM SIGPLAN Notices
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Chapter I: Notes on structured programming
Structured programming
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
WCET analysis of modern processors using multi-criteria optimisation
Empirical Software Engineering
Predicting software complexity by means of evolutionary testing
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
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Test case design is the most important test activity with respect to test quality. For this reason, a large number of testing methods have been developed to assist the tester with the definition of appropriate, error-sensitive test data. Evolutionary testing is a promising approach for automating structure-oriented test case design completely. In many experiments, high coverage degrees were reached using evolutionary testing. However, evolutionary testing is not equally well applicable to different test objects. For example, evolutionary testing of a test object with complex predicates might fail. In order to assess the difficulty of a test object for evolutionary testing, software measures can be used. The knowledge provided by software measurements could lead to a significant increase in efficiency of evolutionary testing. In this paper, we investigate the suitability of structure-based complexity measures for the assessment of whether or not evolutionary testing can be performed successfully for a given test object.