Mutation-driven generation of unit tests and oracles
Proceedings of the 19th international symposium on Software testing and analysis
Solving string constraints lazily
Proceedings of the IEEE/ACM international conference on Automated software engineering
Reducing qualitative human oracle costs associated with automatically generated test data
Proceedings of the First International Workshop on Software Test Output Validation
FlagRemover: A testability transformation for transforming loop-assigned flags
ACM Transactions on Software Engineering and Methodology (TOSEM)
Generating parameterized unit tests
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Strong higher order mutation-based test data generation
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Test data generation: a hybrid approach using cuckoo and tabu search
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Automated web application testing using search based software engineering
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Search-based system testing: high coverage, no false alarms
Proceedings of the 2012 International Symposium on Software Testing and Analysis
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
AUSTIN: An open source tool for search based software testing of C programs
Information and Software Technology
Efficient coverage of parallel and hierarchical stateflow models for test case generation
Software Testing, Verification & Reliability
Search based software test data generation for structural testing: a perspective
ACM SIGSOFT Software Engineering Notes
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Whilst there is much evidence that both concolic and search based testing can outperform random testing, there has been little work demonstrating the effectiveness of either technique with complete real world software applications.As a consequence, many researchers have doubts not only about the scalability of both approaches but also their applicability to production code. This paper performs an empirical study applying a concolic tool, CUTE, and a search based tool, AUSTIN, to the source code of four large open source applications.Each tool is applied `out of the box'; that is without writing additional code for special handling of any of the individual subjects, or by tuning the tools' parameters.Perhaps surprisingly, the results show that both tools can only obtain at best a modest level of code coverage.Several challenges remain for improving automated test data generators in order to achieve higher levels of code coverage.