Evaluation of safety-critical software
Communications of the ACM
Software testing techniques (2nd ed.)
Software testing techniques (2nd ed.)
Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Automated test-data generation for exception conditions
Software—Practice & Experience
Automatic test data generation for path testing using GAs
Information Sciences: an International Journal
What Is Software Testing? And Why Is It So Hard?
IEEE Software
Generating Software Test Data by Evolution
IEEE Transactions on Software Engineering
Fitness Function Design To Improve Evolutionary Structural Testing
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Automated Framework for Structural Test-Data Generation
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
Data Generation for Path Testing
Software Quality Control
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Evolutionary test data generation: a comparison of fitness functions: Research Articles
Software—Practice & Experience
A multi-objective approach to search-based test data generation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An Immune Genetic Algorithm for Software Test Data Generation
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Detecting buffer overflow via automatic test input data generation
Computers and Operations Research
Evolutionary functional testing
Computers and Operations Research
Observations in using parallel and sequential evolutionary algorithms for automatic software testing
Computers and Operations Research
A tabu search algorithm for structural software testing
Computers and Operations Research
GA-based multiple paths test data generator
Computers and Operations Research
Evolutionary software engineering, a review
Applied Soft Computing
Test data generation approach for basis path coverage
ACM SIGSOFT Software Engineering Notes
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This paper presents a new fitness function to generate test data for a specific single path, which is different from the predicate distance applied by most test data generators based on genetic algorithms (GAs). We define a similarity between the target path and execution path to evaluate the quality of the populations. The problem of the most existing generators is to search only one target data a time, wasting plenty of available interim data. We construct another fitness function combined with the single path function, which can drive GA to complete covering multi-paths to avoid the reduplicate searching and utilize the interim populations for different paths. Several experiments are taken to examine the effectiveness of both the single path and multi-path fitness functions, which evaluate the functions' performance with the convergence ability and consumed time. Results show that the two functions perform well compared with other two typical path-oriented functions and the multi-paths approach retrenches the searching actually.