Automatic Test Data Generation Based on Hierarchical Model
The main discussion on software development is software testing with real data. Software testing is one of the expensive and time consuming processes and many studies have been conducted to facilitate and perform it automatically. One of the most important topics in software testing is developing the test path to general test data and coverage of the generated path. Generation the data for test includes identifying a complex of data which evaluate the test criterion. Optimization methods can be used to solve the problem of data testing. Heuristic search methods especially evolutionary algorithms are cost savings and can be effective in the automated generation of Test data. One of the most important challenges in the development of data tests is lack of full coverage of defined Ranges and ignoring the important parameters of user. In this study, a solution is proposed based on Hierarchical model and ant colony optimization algorithm and model-based testing to faster generation test input data and inserted to the program. Then, the test data will be provided and exported for further studies by using generation data and parallel running ant colony algorithm. The model in this study is based on Markov chain. The results obtained from Markov chain are good choices for studying the viability of the testing process while developing them. Evaluation of the proposed algorithm has shown better performance compared to existing methods in terms of cost, coverage, time and parameters of user.
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