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 error and control-flow exceptions. Furthermore, using our tools, we have uncovered two minor exception-related bugs in the three programs we scrutinized.
Title
Tracking down Exceptions in Standard ML Programs
Published
1998-02-01
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
CSD-98-996
Type
Text
Extent
7 p
Archive
The Engineering Library
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