The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Automated program flaw finding using simulated annealing
Proceedings of the 1998 ACM SIGSOFT international symposium on Software testing and analysis
Automated test-data generation for exception conditions
Software—Practice & Experience
Fitness Function Design To Improve Evolutionary Structural Testing
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Coverage Criteria for Logical Expressions
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Evolutionary testing of classes
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
Evolutionary Testing Using an Extended Chaining Approach
Evolutionary Computation
Automatic Generation of Floating-Point Test Data
IEEE Transactions on Software Engineering
Predicate expression cost functions to guide evolutionary search for test data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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In regulated domain such as aerospace and in safety critical domains, software quality assurance is subject to strict regulation such as the RTCA DO-178B standard. Among other conditions, the DO-178B mandates for the satisfaction of the modified condition/decision coverage (MC/DC) testing criterion for software where failure condition may have catastrophic consequences. MC/DC is a white box testing criterion aiming at proving that all conditions involved in a predicate can influence the predicate value in the desired way. In this paper, we propose a novel fitness function inspired by chaining test data generation to efficiently generate test input data satisfying the MC/DC criterion. Preliminary results show the superiority of the novel fitness function that is able to avoid plateau leading to a behavior close to random test of traditional white box fitness functions.