摘要
测试数据的生成是一个复杂的问题,且其技术和方法还不成熟。在生成最小测试用例集过程中,为了避免基本遗传算法对已经满足测试需求的测试用例重复进行遗传操作,文中在基本遗传算法的基础上,最大提高遗传算法的稳定性,提出最大稳定遗传算法(LSGA)。该算法能很好地保证种群的最大稳定性,提高搜索性能,最后对该算法从概率角度理论证明其优越性。实例分析表明,利用该算法能较快生成最小测试用例集,从而实现对测试目标的充分测试,提高测试效率,降低测试成本。
Test data generation is a complicated problem and its method and technique is not mature. In the process of the minimum test case generation,the Largest Steady Genetic Algorithm（ LSGA） is proposed to improve the stability greatly,which is based on the basic genetic algorithm,in order to avoid repeat genetic manipulation of test case which has been met the testing requirement. This algorithm can guarantee the largest population stability and improve the search performance. Contrasted with the genetic algorithm,its superiority is proved from the perspective of the probability. Example analysis shows that using the proposed algorithm can rapidly generate minimum test case sets,achieving the target of the full test,improving the test efficiency and reducing test cost.
出处
《计算机技术与发展》
2016年第4期86-89,共4页
Computer Technology and Development
基金
国家自然科学基金专项基金项目(81141073)
安徽省科技计划项目(1301042203)
安徽省高校省级自然科学研究重点项目(KJ2015A241)
芜湖市科技计划项目(2012hm35-1)
