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Detecting Memory Access Errors with Flow-Sensitive Conditional Range Analysis

机译:通过流动敏感条件范围分析检测内存访问误差

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Accessing an out-of-bounds memory address can lead to nondeter-ministic behaviors or elusive crashes. Static analysis can detect memory access errors from program source codes without runtime overhead, but existing techniques are either very imprecise or exponential cost. This paper proposes a precise and effective method to detect memory access errors. Firstly, it generates a state for each statement with a flow-sensitive, inter-procedural algorithm. A state includes not only range constraints like the traditional range analysis, but also occurrence conditions of the range constraints. Secondly, it solves states of memory access statement to evaluate the sizes of accessed memory bounds. The costs of state generation and state resolution are polynomial. We have implemented a prototype of the analysis method. Applied to 7 popular programs, the prototype found 40 memory access errors with a high precision of 80%.
机译:访问界限内存地址可能会导致非表 - 部门行为或难以捉摸的崩溃。静态分析可以从程序源代码中检测内存访问错误而无需运行时开销,但现有技术是非常不精确或指数的成本。本文提出了一种精确且有效的方法来检测内存访问错误。首先,它为具有流动敏感的,过程间算法的每个语句生成一个状态。状态不仅包括像传统范围分析的范围限制,还包括范围限制的发生条件。其次,它解决了内存访问语句的状态,以评估访问的内存界限的大小。国家生成和州分辨率的成本是多项式。我们已经实现了分析方法的原型。应用于7个流行的节目,原型发现40个内存访问错误,高精度为80%。

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