Optimization of Test Data for Basis Path Testing using Artificial Intelligence Techniques
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Summary
Experimental results show that the test data generated using Clonal selection algorithm was much more effective in achieving better code coverage over Genetic algorithm and Particle swarm optimization.
- Type
- dissertation
- Published
- 2013-01-01
- Cited by
- 1
- References
- 43
- OpenAlex
- https://openalex.org/W129696151
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52233360
Keywords
Computer science, Code coverage, Test data, Data mining, Test Management Approach
References
- Application of Genetic Algorithm in Software Testing
- Object-Oriented Modeling and Design with UML
- Is coverage a good measure of testing effectiveness?: An assessment using branch coverage and Random testing
- Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
- Automatic Test Data Generation for Path Testing Using Genetic Algorithms
- GA-based multiple paths test data generator
- An Efficient Automated Test Data Generation Method
- Systematic mistake analysis of digital computer programs
- Automatic path test data generation based on GA-PSO
- An automatic data generating system for data base simulation and testing
- On the Automated Generation of Program Test Data
- Automatic Generation of Random Self-Checking Test Cases
- Analysis of test case coverage using data mining technique
- A path-oriented test data generation approach for automatic software testing
- Automatic test data generation for path testing using GAs
- Automatic generation of software test data based on hybrid particle swarm genetic algorithm
- A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions
- Test coverage and post-verification defects: A multiple case study
- Enhancing Software Testing by Judicious Use of Code Coverage Information
- On the Testing Maturity of Software Producing Organizations
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