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Test selection for result inspection via mining predicate rules

机译:通过挖掘谓词规则进行结果检查的测试选择

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It is labor-intensive to manually verify the outputs of a large set of tests that are not equipped with test oracles. Test selection helps to reduce this cost by selecting a small subset of tests that are likely to reveal faults. A promising approach is to dynamically mine operational models as potential test oracles and then select tests that violate them. Existing work mines operational models from verified passing tests based on dynamic invariant detection. In this paper, we propose to mine common operational models, which are not always true in all observed traces, from a set of unverified tests based on mining predicate rules. Specifically, we collect values of simple predicates at runtime and then generate and evaluate predicate rules as potential operational models after running all the tests. We then select tests that violate the mined predicate rules for result inspection. Preliminary results on the Siemens suite and the grep program show the effectiveness of our approach.
机译:手动验证未配备测试oracle的大量测试的输出是劳动密集型的。选择测试可以通过选择一小部分可能揭示故障的测试来帮助降低成本。一种有前途的方法是动态地将操作模型挖掘为潜在的测试预告片,然后选择违反它们的测试。现有工作从基于动态不变检测的经过验证的合格测试中挖掘操作模型。在本文中,我们建议从一组基于挖掘谓词规则的未经验证的测试中挖掘出通用的操作模型,这些模型在所有观察到的迹线中并不总是正确的。具体来说,我们在运行时收集简单谓词的值,然后在运行所有测试后将谓词规则生成并评估为潜在的操作模型。然后,我们选择违反挖掘的谓词规则的测试进行结果检查。西门子套件和grep程序的初步结果表明了我们方法的有效性。

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