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Fully Automated Compiler Testing of a Reasoning Engine via Mutated Grammar Fuzzing

机译:通过变异语法模糊测试对推理引擎进行全自动编译器测试

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A reasoning engine infers logical consequences from a set of fixed axioms and observations. However, before it can make an inference, it must compile the axioms and observations which are given in a predefined format. Any attempt to test the correctness of a reasoning engine assumes that it compiles inputs correctly, but that may not be the case. In this work, we implement a mutated grammar fuzzer to automatically generate tests for the compilation stage of Assumption-based Truth Maintenance System (ATMS), a reasoning engine for model-based diagnosis. We also implement a recognizer as an oracle and automatically evaluate the correctness of compiler output. We automatically generate, execute, and evaluate more than a million tests in two weeks. We show that while tests generated from the true grammar of ATMS find no faults, tests generated from mutated grammars uncover an important fault in the compiler. We also show that mutated grammars achieve higher code coverage with fewer tests and the original grammar cannot cover any code that is not covered by mutated grammars. To the best of our knowledge, ours is the first work that provides a practical implementation and evaluation of a mutated grammar fuzzer. We make the implementation available online along with small examples, tests generated for this paper, and steps to reproduce our experiments.
机译:推理引擎从一组固定的公理和观察结果推断出逻辑结果。但是,在进行推断之前,必须编译以预定义格式给出的公理和观察结果。任何测试推理引擎正确性的尝试均假定其正确编译了输入,但事实并非如此。在这项工作中,我们实现了一个变异的语法模糊器,可以自动为基于假设的真相维护系统(ATMS)的编译阶段生成测试,该系统是基于模型的诊断的推理引擎。我们还将实现识别器作为oracle,并自动评估编译器输出的正确性。我们在两周内自动生成,执行和评估超过一百万个测试。我们表明,虽然从ATMS的真实语法生成的测试没有发现错误,但是从变异语法生成的测试却发现了编译器中的重要错误。我们还表明,变异语法使用较少的测试即可实现更高的代码覆盖率,并且原始语法无法覆盖变异语法未覆盖的任何代码。据我们所知,我们是第一项对变异的语法模糊器进行实际实施和评估的工作。我们将在线提供该实现以及一些小示例,为本文生成的测试以及重现我们的实验的步骤。

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