Concurrently searching branches in software tests generation through multitask evolution
Concurrently searching branches in software tests generation through multitask evolution
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DOI：
10.1109/ssci.2016.7850040
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Ramón Sagarna;Y. Ong
中科院分区：
文献类型：
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作者：
Ramón Sagarna;Y. Ong
多任务进化计算（MT-EC）最近已被确定为一个潜在的有用的范例，为重要的现实世界的领域。其中一个领域就是软件测试领域。虽然已经存在一些渐进的方法，但缺乏能够利用不同来源的知识来加强搜索过程的战略。在这项工作中，我们专注于分支测试和探索的MT-EC的能力，引导搜索利用间的分支信息。据我们所知，这是MT-EC的第一个应用程序，以现实世界的问题，两个以上的任务。准确地说，我们表明，选择，连同用于比较个人的偏好关系，形成一种机制，能够实现并行搜索的分支，同时利用分支间的知识在这个过程中。此外，我们证明了转移的强度可以改变与实施的选择。基准程序的实验结果表明，MT-EC可以特别有用的情况下，预算的搜索过程是有限的。
Multitask evolutionary computation (MT-EC) has been recently identified as a potentially useful paradigm for significant real-world domains. One such domain is the field of software testing. Although a number of evolutionary approaches exist already, there is a lack of strategies that can leverage knowledge from different sources to enhance the search process. In this work, we focus on branch testing and explore the capability of MT-EC to guide the search by exploiting inter-branch information. To the best of our knowledge, this is the first application of MT-EC to real-world problems with more than two tasks. Precisely, we evince that selection, together with the preference relation used to compare individuals, form a mechanism capable of achieving a concurrent search for the branches while exploiting inter-branch knowledge in the process. Further, we demonstrate that the intensity of the transfer can be altered with the implemented selection. The experimental results on benchmark programs suggest that MT-EC can be specially useful in situations where the budget for the search process is limited.