GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
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
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
Search based software testing of object-oriented containers
Information Sciences: an International Journal
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
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
IEEE Transactions on Software Engineering
Longer is Better: On the Role of Test Sequence Length in Software Testing
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
Exploiting program dependencies for scalable multiple-path symbolic execution
Proceedings of the 19th international symposium on Software testing and analysis
It is Not the Length That Matters, It is How You Control It
ICST '11 Proceedings of the 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation
A multiple-population genetic algorithm for branch coverage test data generation
Software Quality Control
Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Search-Based Test Input Generation for String Data Types Using the Results of Web Queries
ICST '12 Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
EXSYST: search-based GUI testing
Proceedings of the 34th International Conference on Software Engineering
IEEE Transactions on Software Engineering
Cellular-genetic test data generation
ACM SIGSOFT Software Engineering Notes
Artificial life and cellular automata based automated test case generator
ACM SIGSOFT Software Engineering Notes
Generating test data for both path coverage and fault detection using genetic algorithms
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Generating test data covering multiple paths using multi-population parallel genetic algorithms is a considerable important method. The premise on which the method above is efficient is appropriately grouping target paths. Effective methods of grouping target paths, however, have been absent up to date. The problem of grouping target paths for generation of test data covering multiple paths is investigated, and a novel method of grouping target paths is presented. In this method, target paths are divided into several groups according to calculation resources available and similarities among target paths, making a small difference in the number of target paths belonging to different groups, and a great similarity among target paths in the same group. After grouping these target paths, a mathematical model is built for parallel generation of test data covering multiple paths, and a multi-population genetic algorithm is adopted to solve the model above. The proposed method is applied to several benchmark or industrial programs, and compared with a previous method. The experimental results show that the proposed method can make full use of calculation resources on the premise of meeting the requirement of path coverage, improving the efficiency of generating test data.