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

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 [Yi94] (based on abstract interpretation [CC77]) 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. At time of this writing, 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.