Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the third international conference on Genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Empirical Software Engineering
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Searching for Cognitively Diverse Tests: Towards Universal Test Diversity Metrics
ICSTW '08 Proceedings of the 2008 IEEE International Conference on Software Testing Verification and Validation Workshop
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Using program data-state scarcity to guide automatic test data generation
Software Quality Control
EvoSuite: automatic test suite generation for object-oriented software
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
An Analysis and Survey of the Development of Mutation Testing
IEEE Transactions on Software Engineering
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
Estimating the evolution direction of populations to improve genetic algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
IEEE Transactions on Software Engineering
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The effectiveness of evolutionary test case generation based on Genetic Algorithms (GAs) can be seriously impacted by genetic drift, a phenomenon that inhibits the ability of such algorithms to effectively diversify the search and look for alternative potential solutions. In such cases, the search becomes dominated by a small set of similar individuals that lead GAs to converge to a sub-optimal solution and to stagnate, without reaching the desired objective. This problem is particularly common for hard-to-cover program branches, associated with an extremely large solution space. In this paper, we propose an approach to solve this problem by integrating a mechanism for orthogonal exploration of the search space into standard GA. The diversity in the population is enriched by adding individuals in orthogonal directions, hence providing a more effective exploration of the solution space. To the best of our knowledge, no prior work has addressed explicitly the issue of evolution direction based diversification in the context of evolutionary testing. Results achieved on 17 Java classes indicate that the proposed enhancements make GA much more effective and efficient in automating the testing process. In particular, effectiveness (coverage) was significantly improved in 47% of the subjects and efficiency (search budget consumed) was improved in 85% of the subjects on which effectiveness remains the same.