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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Search-Based Test Data Generator for Data-Flow Dependencies Using Dominance Concepts, Branch Distance and Elitism
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Search-Based Test Data Generator for Data-Flow Dependencies Using Dominance Concepts, Branch Distance and Elitism

机译：基于搜索的测试数据发生器，用于数据流依赖性使用优势概念，分支距离和精英主义

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摘要

Software testing is a complex and expensive activity of the software development life cycle. Software testing includes test data generation according to a test adequacy criterion. The use of search-based techniques has been the focus of researchers to automate the process of software test data generation for structural control-flow criteria. Automating test data generation remains a challenging problem for more robust adequacy criterion such as satisfying data-flow dependencies of a program. This study proposes a search-based approach that generates test data for dataflow dependencies of a program using dominance concepts, branch distance, and elitism. Genetic algorithm is used for the proposed approach and Gray encoding is used to encode test data. A set of subject programs is taken from the research literature to evaluate efficiency and effectiveness of the proposed approach. For the proposed approach, the measures considered are the mean number of generations and mean percentage coverage achieved. The performance of the proposed approach is evaluated by comparing the results with those of random search and earlier studies on data-flow testing. Over several experiments, it is shown that the proposed approach performed significantly better than random search and earlier studies with respect to data-flow test data generation and optimization. There is an increasing performance gap for more complex subject programs.
机译：软件测试是软件开发生命周期的复杂和昂贵的活动。软件测试包括根据测试充足性标准的测试数据生成。使用搜索技术的使用是研究人员的焦点，以自动化结构控制流程标准的软件测试数据生成过程。自动化测试数据生成对于更强大的充分性标准，诸如满足程序的数据流依赖性，仍然是一个具有挑战性的问题。本研究提出了一种基于搜索的方法，用于使用主导概念，分支距离和精英主义来生成程序的数据流依赖性的测试数据。遗传算法用于所提出的方法，灰色编码用于编码测试数据。从研究文献中取出一组主题计划，以评估所提出的方法的效率和有效性。为提出的方法，所考虑的措施是几代人数和均衡百分比覆盖。通过将结果与随机搜索和更早期的数据流测试的研究进行比较来评估所提出的方法的性能。在几个实验中，示出了所提出的方法比数据流测试数据生成和优化显着地优于随机搜索和早期研究。对于更复杂的主题程序，性能差距越来越大。

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