复合断层理论

Rahul Gopinath, Carlos Jensen, Alex Groce
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引用次数: 26

摘要

对于特定的测试输入,当一个故障的影响用来掩盖另一个故障的影响时,就会发生故障屏蔽。测试从业者依靠耦合效应来确保故障掩蔽是罕见的。它指出,复杂故障以这样一种方式耦合到简单故障,即检测程序中所有简单故障的测试数据集将检测到高百分比的复杂故障。虽然这种效应已经得到了实证评估,但我们对耦合效应的理论理解尚不完整。Wah提出了有限双射(或近双射)函数的耦合效应理论,这些函数具有相同的域和上域,候选函数假设均匀分布。然而,这个模型被批评为过于简单,无法对实际系统建模,因为它没有考虑到实际程序中的不同域和上域,也没有考虑到语法邻域。提出了一种新的一般函数(具有一定约束条件)的故障耦合理论。我们证明了有两种故障相互作用,其中只有弱相互作用可以用耦合效应理论来建模。强相互作用会产生与原断层在语义上不同的断层。因此,这些错误应该被认为是独立的原子错误。我们的分析表明,即使考虑到程序的句法邻域的影响,该理论仍然成立。为了验证我们的假设,我们分析了许多具有真实故障的现实世界程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Theory of Composite Faults
Fault masking happens when the effect of one fault serves to mask that of another fault for particular test inputs. The coupling effect is relied upon by testing practitioners to ensure that fault masking is rare. It states that complex faults are coupled to simple faults in such a way that a test data set that detects all simple faults in a program will detect a high percentage of the complex faults. While this effect has been empirically evaluated, our theoretical understanding of the coupling effect is as yet incomplete. Wah proposed a theory of the coupling effect on finite bijective (or near bijective) functions with the same domain and co-domain and assuming a uniform distribution for candidate functions. This model, however, was criticized as being too simple to model real systems, as it did not account for differing domain and co-domain in real programs, or for the syntactic neighborhood. We propose a new theory of fault coupling for general functions (with certain constraints). We show that there are two kinds of fault interactions, of which only the weak interaction can be modeled by the theory of the coupling effect. The strong interaction can produce faults that are semantically different from the original faults. These faults should hence be considered as independent atomic faults. Our analysis shows that the theory holds even when the effect of the syntactic neighborhood of the program is considered. We analyze numerous real-world programs with real faults to validate our hypothesis.
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