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Estimating uncaught exceptions in Standard ML programs from type-based equations

机译:从基于类型的方程式估算标准ML程序中的未捕获异常

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We present a static analysis that detects potential runtime exceptions that are raised and never handled inside Standard ML (SML) programs. Contrary to our earlier method (Yi, 1994) based on abstract interpretation, where the input program's control flow is simultaneously computed while our exception analysis progresses, we separate the two phases in a manner similar to conventional data flow analysis. Before the exception analysis begins, we first estimate the input program's control flow from the type information from SML/NJ compiler. Based on this call-graph structure, exception flow is specified as a set of equations, whose solution is computed using an iterative least fixpoint method. A prototype of this analysis is applied to two realistic SML programs (ML-LEX and OR-SML core) and is 3 or 40 times faster than the earlier method and saves memory by 35 or 65 percent.
机译:我们提供了一个静态分析,该分析可检测潜在的运行时异常,这些异常在标准ML(SML)程序中引发且从未处理。与我们以前基于抽象解释的方法(Yi,1994)相反,后者在异常分析进行时同时计算输入程序的控制流,我们以类似于常规数据流分析的方式将两个阶段分开。在异常分析开始之前,我们首先从SML / NJ编译器的类型信息估计输入程序的控制流。基于此调用图结构,将异常流指定为一组方程式,使用迭代最小定点方法计算其解。此分析的原型应用于两个实际的SML程序(ML-LEX和OR-SML内核),比早期方法快3或40倍,并节省了35%或65%的内存。

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