Finding Interaction Faults Adaptively Using Distance-Based Strategies

Renée C. Bryce, C. Colbourn, D. Kuhn
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引用次数: 10

Abstract

Software systems are typically large and exhaustive testing of all possible input parameters is usually not feasible. Testers select tests that they anticipate may catch faults, yet many unanticipated faults may be overlooked. This work complements current testing methodologies by adaptively dispensing one-test-at-a-time, where each test is as “distant” as possible from previous tests. Two types of distance measures are explored: (1) distance defined in relation to combinations of parameter-values not previously tested together and (2) distance computed as the maximum minimal Hamming distance from previous tests. Experiments compare the effectiveness of these two types of distance-based tests and random tests. Experiments include simulations, as well as examination of instrumented data from an actual system, the Traffic Collision Avoidance System (TCAS). Results demonstrate that the two instantiations of distance-based tests often find more faults sooner and in fewer tests than randomly generated tests.
基于距离的策略自适应发现交互故障
软件系统通常很大,对所有可能的输入参数进行详尽的测试通常是不可行的。测试人员选择他们预期可能捕获错误的测试,但是许多未预料到的错误可能被忽略。这项工作通过自适应地分配一次一个测试来补充当前的测试方法,其中每个测试都尽可能与以前的测试相同 - œdistantâ -”。探索了两种类型的距离度量:(1)与先前未一起测试的参数值组合相关的距离定义;(2)从先前测试中计算的最大最小汉明距离。实验比较了这两种基于距离的测试和随机测试的有效性。实验包括模拟,以及检查来自实际系统的仪表数据,即交通碰撞避免系统(TCAS)。结果表明,与随机生成的测试相比,基于距离的测试的两个实例通常在更少的测试中更快地发现更多的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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