用于bug检测的Python预测分析

Zhaogui Xu, Peng Liu, X. Zhang, Baowen Xu
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引用次数: 23

摘要

Python是一种流行的动态语言,支持快速软件开发。然而,Python程序分析引擎在很大程度上是缺乏的。在本文中,我们提出了一个Python预测分析。它首先收集执行的跟踪,然后将跟踪和未执行的分支编码为符号约束。引入符号变量来表示输入值、它们的动态类型和属性集,以推断它们的变化。解决约束可以识别bug和它们的触发输入。我们的评估表明,由于其基于跟踪的复杂编码设计,该技术在分析具有大量动态特征和外部库调用的现实世界复杂程序方面非常有效。它从11个实际项目中识别出46个bug,其中16个是新bug。所有报告的bug都是真正的阳性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Python predictive analysis for bug detection
Python is a popular dynamic language that allows quick software development. However, Python program analysis engines are largely lacking. In this paper, we present a Python predictive analysis. It first collects the trace of an execution, and then encodes the trace and unexecuted branches to symbolic constraints. Symbolic variables are introduced to denote input values, their dynamic types, and attribute sets, to reason about their variations. Solving the constraints identifies bugs and their triggering inputs. Our evaluation shows that the technique is highly effective in analyzing real-world complex programs with a lot of dynamic features and external library calls, due to its sophisticated encoding design based on traces. It identifies 46 bugs from 11 real-world projects, with 16 new bugs. All reported bugs are true positives.
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