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Identifying Modules Which Do Not Propagate Errors

机译:识别不传播错误的模块

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Our goal is to identify software modules that have some locations which do not propagate errors induced by a suite of test cases. This paper focuses on whether or not data state errors can propagate from a location in the code to the outputs or observable data state during random testing with inputs drawn from an operational distribution. If a code-location's probability of propagation is estimated to be zero, then a fault in that location could escape detection during testing. Because testing is never exhaustive, there is a risk that failures due to such latent faults could occur during operations. Fault injection is a technique for directly measuring the probability of propagation. However, measurement for every location in the code of a full-scale program is often prohibitively computation-intensive. Our objective is a practical, useful alternative to direct measurement. We present empirical evidence that static software product metrics can be useful for identifying software modules where the effects of a fault in that module are not observable. A case study of an intricate computer game program revealed a useful empirical relationship between static software product metrics and propagation of errors. The case study program was an order of magnitude larger than previously reported studies.
机译:我们的目标是识别具有一些未传播由一套测试用例引起的错误的位置的软件模块。本文侧重于在随机测试期间从代码中的位置传播到数据状态误差是否可以从代码中的位置传播到输出或可观察数据状态。如果估计代码位置的传播概率为零,则该位置的故障可能会在测试期间转义检测。由于测试永不穷,因此在运营期间可能会出现由于此类潜在故障导致的失败风险。故障注入是一种直接测量传播概率的技术。但是,全尺度程序代码中的每个位置的测量通常都不核实计算密集。我们的目标是直接测量的实用,有用的替代品。我们提出了实证证据,即静态软件产品度量可以用于识别该模块中故障的影响而不是观察到的软件模块。对复杂计算机游戏程序的案例研究显示了静态软件产品度量与错误传播之间的有用实证关系。案例研究计划比以前报告的研究大的数量级。

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