Automated Software Test Data Generation
IEEE Transactions on Software Engineering
The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Machine Learning
IEEE Transactions on Software Engineering
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
Software Testing, Verification & Reliability - UKTest 2005: The Third U.K. Workshop on Software Testing Research
Proceedings of the 2007 international symposium on Software testing and analysis
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
IEEE Transactions on Software Engineering
Theoretical analysis of local search in software testing
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
TestFul: An Evolutionary Test Approach for Java
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
On parameter tuning in search based software engineering
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Bytecode testability transformation
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Mutation-Driven Generation of Unit Tests and Oracles
IEEE Transactions on Software Engineering
The Seed is Strong: Seeding Strategies in Search-Based Software Testing
ICST '12 Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
Sound empirical evidence in software testing
Proceedings of the 34th International Conference on Software Engineering
IEEE Transactions on Software Engineering
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Genetic Algorithms have been successfully applied to the generation of unit tests for classes, and are well suited to create complex objects through sequences of method calls. However, because the neighborhood in the search space for method sequences is huge, even supposedly simple optimizations on primitive variables (e.g., numbers and strings) can be ineffective or unsuccessful. To overcome this problem, we extend the global search applied in the EVOSUITE test generation tool with local search on the individual statements of method sequences. In contrast to previous work on local search, we also consider complex datatypes including strings and arrays. A rigorous experimental methodology has been applied to properly evaluate these new local search operators. In our experiments on a set of open source classes of different kinds (e.g., numerical applications and text processing), the resulting test data generation technique increased branch coverage by up to 32% on average over the normal Genetic Algorithm.