An abstract interpretation for estimating uncaught exceptions in Standard ML programs

Abstract

We present a static analysis that detects potential runtime exceptions that are raised and never handled inside Standard ML(SML) programs. This analysis enhances the software safety by predicting, prior to the program execution, the abnormal termination caused by unhandled exceptions. The analysis is specified as a finite, abstract semantics of an intermediate language. The intermediate language, into which SML programs are translated before the analysis begins, is defined such that the mechanism of SML's exception propagation becomes explicit in its text. This syntactic manipulation makes our analysis easy. Our analysis prototype has been implemented by using an analyzer generator called Z1 and has been used to analyze SML programs consisting of thousand lines. Our analysis is limited to SML programs that are type-correct and are operationally invariant even if the generative nature of SML's data-type and exception declarations is not considered.