Automated Software Test Data Generation
IEEE Transactions on Software Engineering
Algorithm 447: efficient algorithms for graph manipulation
Communications of the ACM
Software Test Data Generation Using the Chaining Approach
Proceedings of the IEEE International Test Conference on Driving Down the Cost of Test
An Automated Framework for Structural Test-Data Generation
ASE '98 Proceedings of the 13th IEEE international conference on Automated software engineering
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Search-based software test data generation: a survey: Research Articles
Software Testing, Verification & Reliability
Automatic State-Based Test Generation Using Genetic Algorithms
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Functional Search-based Testing from State Machines
ICST '08 Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation
Generating Feasible Transition Paths for Testing from an Extended Finite State Machine (EFSM)
ICST '09 Proceedings of the 2009 International Conference on Software Testing Verification and Validation
Aiding Test Case Generation in Temporally Constrained State Based Systems Using Genetic Algorithms
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
A Search-Based Approach for Automatic Test Generation from Extended Finite State Machine (EFSM)
TAIC-PART '09 Proceedings of the 2009 Testing: Academic and Industrial Conference - Practice and Research Techniques
Empirical Study on the Efficiency of Search Based Test Generation for EFSM Models
ICSTW '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification, and Validation Workshops
Information and Software Technology
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This paper presents a new approach to test generation from extended finite state machines using genetic algorithms, by proposing a new fitness function for path data generation. The fitness function that guides the search is crucial for the success of a genetic algorithm; an improvement in the fitness function will reduce the duration of the generation process and increase the success chances of the search algorithm. The paper performs a comparison between the newly proposed fitness function and the most widely used function in the literature. The experimental results show that, for more complex paths, that can be logically decomposed into independent sub-paths, the new function outperforms the previously proposed function and the difference is statistically significant.