通过模糊测试找到规范盲点

Ru Ji, Meng Xu
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引用次数: 0

摘要

经过正式验证的程序只有在其规范(SPEC)中才是正确的。但是如何确保SPEC是完整的并且没有漏洞呢?本文提出了Fast(模糊辅助规格测试的缩写)作为一种可能的解决方案。关键的洞察力是利用和协同“冗余”和“多样性”的正式验证程序进行交叉检查。具体地说,在相同的代码库中,SPEC、实现(CODE)和测试套件都来自相同的业务需求集。因此,如果在CODE和测试用例中捕获了一些意图,但在SPEC中没有,这是SPEC中存在盲点的强烈迹象。Fast以自动化的方式检查SPEC的不完整性问题:它首先通过突变测试定位SPEC差距,即,通过检查CODE变体是否符合原始SPEC。如果是这样,Fast进一步利用测试套件来推断差距是由意图还是错误引入的。根据代码库的大小,Fast可以选择以枚举或演化的方式生成CODE变体。Fast应用于两个开源代码库,这两个代码库以正式验证为特征,并分别帮助确认其SPEC中的13个和21个盲点。这突出了在实际应用程序中普遍存在的SPEC不完整性。
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Finding Specification Blind Spots via Fuzz Testing
A formally verified program is only as correct as its specifications (SPEC). But how to assure that the SPEC is complete and free of loopholes? This paper presents Fast, short for Fuzzing-Assisted Specification Testing, as a potential answer. The key insight is to exploit and synergize the "redundancy" and "diversity" in formally verified programs for cross-checking. Specifically, within the same codebase, SPEC, implementation (CODE), and test suites are all derived from the same set of business requirements. Therefore, if some intention is captured in CODE and test case but not in SPEC, this is a strong indication that there is a blind spot in SPEC.Fast examines the SPEC for incompleteness issues in an automated way: it first locates SPEC gaps via mutation testing, i.e., by checking whether a CODE variant conforms to the original SPEC. If so, Fast further leverages the test suites to infer whether the gap is introduced by intention or by mistake. Depending on the codebase size, Fast may choose to generate CODE variants in either an enumerative or evolutionary way. Fast is applied to two open-source codebases that feature formal verification and helps to confirm 13 and 21 blind spots in their SPEC respectively. This highlights the prevalence of SPEC incompleteness in real-world applications.
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