Dynamic-radius Species-conserving Genetic Algorithm for Test Generation for Structural Testing
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چکیده
Software testing is a critical and labor-intensive activity in software engineering. Much research has been done to help automate test case generation. This research proposes a new approach to structural test case generation. It uses a specialized genetic algorithm called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) to find a structurally complete set of test cases for the Triangle Classification algorithm. DSGA is a Niche Genetic Algorithm (NGA) that uses a short-term memory structure to store optima. Each individual of the NGA represents the inputs for a test case. The fitness function encourages the algorithm to locate test cases that cover large areas of the structure of the program. A shared fitness encourages the NGA to locate other areas of the structure. DSGA is a novel approach to structurally complete test case generation.
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تاریخ انتشار 2017