基于需求的回归测试选择扩展模型

A. Lawanna
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引用次数: 0

摘要

维护软件的问题是考虑在测试套件规模变大的情况下,应该为下一次修改保留哪些测试用例。这使得软件开发的性能下降。提出基于需求的测试用例选择模型的目的是为了提高回归测试选择的能力,特别是为了调节修改后的程序的测试套件的大小,修改后的测试套件会根据特定需求的需要变得更大,包括准备更高的排除故障的能力。它包括五种主要算法,分别是发现重用的测试用例、分类、修改、删除和选择适当的测试用例。本文采用6个程序运行在不同的4个比较研究上,分别采用了选择性、随机和回归检验选择。与传统技术相比,该技术的平均尺寸约为49.78%。此外,它提供的修复错误的百分比比select-all, random和regression test选择算法高,约为0.06-1.32%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Requirement-based Selection Model for the Expansion of Regression Test Selection
Issue of maintaining software is to consider which test cases should be kept for the next modification where the size of test suite gets bigger. This makes performance of software development pull out. The objective of proposing requirement-based test case selection model is to improve ability of regression test selection, in particular, to moderate the size of test suite of the modified program, which gets larger after modification regarding the need of specific requirements, including preparing higher ability of removing faults. It comprises five main algorithms, which are finding reused test case, classifying, revising, deleting, and selecting the appropriate test cases. This paper uses six programs run on different four comparative studies, which are selectall, random, and regression test selection. It gives smaller size than the traditional techniques about 49.78% in average. Besides, it offers percent fixing faults that is higher than select-all, random, and regression test selection algorithm as around 0.06–1.32%.
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