The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Symbolic execution and program testing
Communications of the ACM
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 classes
ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
Branch-Coverage Testability Transformation for Unstructured Programs
The Computer Journal
A unified fitness function calculation rule for flag conditions to improve evolutionary testing
Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering
Evolutionary Testing Using an Extended Chaining Approach
Evolutionary Computation
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
Improving Evolutionary Testing in the Presence of Function-Assigned Flags
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
Automatic Generation of Floating-Point Test Data
IEEE Transactions on Software Engineering
Evolutionary testing of flag conditions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Bytecode testability transformation
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
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Evolutionary structural testing, an approach to automatically generate relevant unit test data, encounters difficulties when the software being tested contains boolean variables. This issue, known as the flag problem, has been studied by many researchers. However, previous work does not address the issue of function-assigned flags which constitutes a special type of flag problem that often occurs in the context of object-orientation. This paper elaborates on a new approach to the flag problem that can also handle function-assigned flags while being applicable to the conventional flag problem, as well. It relies on a code transformation that leads to an improved fitness landscape which provides better guidance to the evolutionary search. We present seven case studies including a fitness landscape analysis and experimental results. The results show that the suggested code transformation improves evolutionary structural testing in the presence of function-assigned flags.