Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
ADTEST: A Test Data Generation Suite for Ada Software Systems
IEEE Transactions on Software Engineering
Automated test data generation using an iterative relaxation method
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
Automatic test data generation for path testing using GAs
Information Sciences: an International Journal
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
What Is Software Testing? And Why Is It So Hard?
IEEE Software
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Suitability of Evolutionary Algorithms for Evolutionary Testing
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using genetic algorithms for test case generation in path testing
ATS '00 Proceedings of the 9th Asian Test Symposium
Identification of Potentially Infeasible Program Paths by Monitoring the Search for Test Data
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
A logarithmic poisson execution time model for software reliability measurement
ICSE '84 Proceedings of the 7th international conference on Software engineering
An extension to the cyclomatic measure of program complexity
ACM SIGPLAN Notices
The Art of Software Testing
Data Generation for Path Testing
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Search-based software test data generation: a survey: Research Articles
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The species per path approach to SearchBased test data generation
Proceedings of the 2006 international symposium on Software testing and analysis
The impact of input domain reduction on search-based test data generation
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
On the Automated Generation of Program Test Data
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Observations in using parallel and sequential evolutionary algorithms for automatic software testing
Computers and Operations Research
GA-based multiple paths test data generator
Computers and Operations Research
Handling Constraints for Search Based Software Test Data Generation
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
Automatic Path-Oriented Test Data Generation Using a Multi-population Genetic Algorithm
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Automated test data generation using a scatter search approach
Information and Software Technology
Comparison of Two Fitness Functions for GA-Based Path-Oriented Test Data Generation
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
Experimental Study on GA-Based Path-Oriented Test Data Generation Using Branch Distance Function
IITA '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 01
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
IEEE Transactions on Software Engineering
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
Genetic Algorithm Based Path Testing: Challenges and Key Parameters
WCSE '10 Proceedings of the 2010 Second World Congress on Software Engineering - Volume 01
Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
IEEE Transactions on Software Engineering
EvoSuite at the SBST 2013 Tool Competition
ICSTW '13 Proceedings of the 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation Workshops
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Context: Evolutionary algorithms have proved to be successful for generating test data for path coverage testing. However in this approach, the set of target paths to be covered may include some that are infeasible. It is impossible to find test data to cover those paths. Rather than searching indefinitely, or until a fixed limit of generations is reached, it would be desirable to stop searching as soon it seems likely that feasible paths have been covered and all remaining un-covered target paths are infeasible. Objective: The objective is to develop criteria to halt the evolutionary test data generation process as soon as it seems not worth continuing, without compromising testing confidence level. Method: Drawing on software reliability growth models as an analogy, this paper proposes and evaluates a method for determining when it is no longer worthwhile to continue searching for test data to cover un-covered target paths. We outline the method, its key parameters, and how it can be used as the basis for different decision rules for early termination of a search. Twenty-one test programs from the SBSE path testing literature are used to evaluate the method. Results: Compared to searching for a standard number of generations, an average of 30-75% of total computation was avoided in test programs with infeasible paths, and no feasible paths were missed due to early termination. The extra computation in programs with no infeasible paths was negligible. Conclusions: The method is effective and efficient. It avoids the need to specify a limit on the number of generations for searching. It can help to overcome problems caused by infeasible paths in search-based test data generation for path testing.