Tracking down Exceptions in Standard ML Programs

Authors:
Manuel Fahndrich;Jeffrey Foster;Jason Cu;Alexander Aiken
Affiliations:
-;-;-;-
Venue:
Tracking down Exceptions in Standard ML Programs
Year:
1998

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Abstract

We describe our experiences 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 control-flow exceptions. Furthermore, using our tools, we have uncovered two minor exception-related bugs in the three programs we scrutinized.