Tracking down Exceptions in Standard ML Programs

M. Fähndrich, J. Foster, Alexander Aiken, and Jason Cu

Abstract

We describe our experience with an exception analysis tool for Standard ML. Information about exceptions gathered by the analysis is visualized using PAM, a program visualization tool for EMACS. We study the results of the analysis of three well-known programs, classifying exceptions as assertion failures, error exceptions, control-flow exceptions, and pervasive exceptions. Even though the analysis is often conservative and reports many spurious exceptions, we have found it useful for checking the consistency of error and control-flow exceptions. Furthermore, using our tools, we have uncovered two minor exception-related bugs in the three programs we scrutinized.

Details

Publication typeTechReport
NumberUCB//CSD-96-996
InstitutionUniversity of California, Berkeley
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