Sigma*: symbolic learning of input-output specifications

M. Botincan, Domagoj Babic
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引用次数: 64

Abstract

We present Sigma*, a novel technique for learning symbolic models of software behavior. Sigma* addresses the challenge of synthesizing models of software by using symbolic conjectures and abstraction. By combining dynamic symbolic execution to discover symbolic input-output steps of the programs and counterexample guided abstraction refinement to over-approximate program behavior, Sigma* transforms arbitrary source representation of programs into faithful input-output models. We define a class of stream filters---programs that process streams of data items---for which Sigma* converges to a complete model if abstraction refinement eventually builds up a sufficiently strong abstraction. In other words, Sigma* is complete relative to abstraction. To represent inferred symbolic models, we use a variant of symbolic transducers that can be effectively composed and equivalence checked. Thus, Sigma* enables fully automatic analysis of behavioral properties such as commutativity, reversibility and idempotence, which is useful for web sanitizer verification and stream programs compiler optimizations, as we show experimentally. We also show how models inferred by Sigma* can boost performance of stream programs by parallelized code generation.
Sigma*:输入-输出规范的符号学习
我们提出Sigma*,一种学习软件行为符号模型的新技术。Sigma*解决了通过使用符号猜想和抽象来综合软件模型的挑战。通过结合动态符号执行来发现程序的符号输入-输出步骤和反例指导抽象细化以过度近似程序行为,Sigma*将程序的任意源表示转换为忠实的输入-输出模型。我们定义了一类流过滤器——处理数据项流的程序——如果抽象细化最终建立了一个足够强的抽象,Sigma*就会收敛到一个完整的模型。换句话说,Sigma*相对于抽象是完整的。为了表示推断的符号模型,我们使用了一种符号换能器的变体,它可以有效地组成和等价性检查。因此,Sigma*可以完全自动地分析行为特性,如交换性、可逆性和幂等性,这对于网络消毒验证和流程序编译器优化非常有用,正如我们在实验中所展示的那样。我们还展示了Sigma*推断的模型如何通过并行代码生成来提高流程序的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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