Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Art of Software Testing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Empirical Software Engineering
Empirical Software Engineering
Pareto efficient multi-objective test case selection
Proceedings of the 2007 international symposium on Software testing and analysis
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Applying particle swarm optimization to software testing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage
APSEC '07 Proceedings of the 14th Asia-Pacific Software Engineering Conference
Evolutionary functional testing
Computers and Operations Research
GA-based multiple paths test data generator
Computers and Operations Research
An experimental study of four typical test suite reduction techniques
Information and Software Technology
Search based software testing of object-oriented containers
Information Sciences: an International Journal
A rigorous approach towards test case generation
Information Sciences: an International Journal
Automatic, evolutionary test data generation for dynamic software testing
Journal of Systems and Software
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
Tabu search-based metaheuristic algorithm for software system reliability problems
Computers and Operations Research
Evolutionary White-Box Software Test with the EvoTest Framework: A Progress Report
ICSTW '09 Proceedings of the IEEE International Conference on Software Testing, Verification, and Validation Workshops
Adaptive Random Testing: The ART of test case diversity
Journal of Systems and Software
Evolutionary generation of test data for many paths coverage based on grouping
Journal of Systems and Software
An Analysis and Survey of the Development of Mutation Testing
IEEE Transactions on Software Engineering
Generating test data for both paths coverage and faults detection using genetic algorithms
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Grouping target paths for evolutionary generation of test data in parallel
Journal of Systems and Software
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The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current studies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolutionary algorithmis employed to solve the formulatedmodel, and several types of fault detectionmethods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary optimization method in the following three aspects: the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.