Fixed Sized Candidate Set (FSCS) is the first of a series of methods proposed to enhance the effectiveness of random testing (RT) referred to as Adaptive Random Testing methods or ARTs. Since its inception, test case generation overheads have been a major drawback to the success of ART. In FSCS, the bulk of this cost is embedded in distance computations between a set of randomly generated candidate test cases and previously executed but unsuccessful test cases. Consequently, FSCS is caught in a logical trap of probing the distances between every candidate and all executed test cases before the best candidate is determined. Using data mining, however, we discovered that about 50% of all valid test cases are encountered much earlier in the distance computations process but without any benefit of a hindsight, FSCS is unable to validate them; a wild goose chase. This paper then uses this information to propose a new strategy that predictively and proactively selects valid candidates anywhere during the distance computation process without vetting every candidate. Theoretical analysis, simulations and experimental studies conducted led to a similar conclusion: 25% of the distance computations are wasteful and can be discarded without any repercussion on effectiveness.

中文翻译：

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一种主动的测试用例选择方法——自适应随机测试的有效实现

固定大小的候选集 (FSCS) 是为提高随机测试 (RT) 的有效性而提出的一系列方法中的第一个，称为自适应随机测试方法或 ART。自开始以来，测试用例生成开销一直是 ART 成功的主要缺点。在 FSCS 中，大部分成本嵌入在一组随机生成的候选测试用例与之前执行但不成功的测试用例之间的距离计算中。因此，FSCS 陷入了一个逻辑陷阱，即在确定最佳候选者之前探查每个候选者与所有已执行测试用例之间的距离。然而，使用数据挖掘，我们发现大约 50% 的有效测试用例是在距离计算过程的早期遇到的，但没有任何事后诸葛亮，FSCS 无法验证它们；一场野鹅追逐。然后，本文使用这些信息提出了一种新策略，该策略在距离计算过程中的任何地方预测性和主动地选择有效候选者，而无需审查每个候选者。进行的理论分析、模拟和实验研究得出了类似的结论：25% 的距离计算是浪费的，可以丢弃而不会对有效性产生任何影响。