Compile-time detection of 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 programs. This analysis enhances the software safety by predicting, prior to the program execution, the abnormal termination caused by unhandled exceptions.Our analysis prototype has been implemented by using a semantics-based analyzer generator and has been successfully tested with real Standard ML programs consisting of thousand lines.We introduce semantic sparse analysis to reduce the analysis cost without compromising the analysis accuracy. In this method, expressions will only be analyzed when their evaluations are relevant to our analysis.