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dc.contributor.advisorMichael Ernst
dc.contributor.authorArtzi, Shay
dc.contributor.authorErnst, Michael D.
dc.contributor.authorGlasser, David
dc.contributor.authorKiezun, Adam
dc.contributor.otherProgram Analysis
dc.date.accessioned2006-09-18T17:55:18Z
dc.date.available2006-09-18T17:55:18Z
dc.date.issued2006-09-17
dc.identifier.otherMIT-CSAIL-TR-2006-065
dc.identifier.urihttp://hdl.handle.net/1721.1/33968
dc.description.abstractKnowing which method parameters may be mutated during a method'sexecution is useful for many software engineering tasks. We presentan approach to discovering parameter immutability, in which severallightweight, scalable analyses are combined in stages, with each stagerefining the overall result. The resulting analysis is scalable andcombines the strengths of its component analyses. As one of thecomponent analyses, we present a novel, dynamic mutability analysisand show how its results can be improved by random input generation.Experimental results on programs of up to 185 kLOC demonstrate that,compared to previous approaches, our approach increases both scalabilityand overall accuracy.
dc.format.extent10 p.
dc.format.extent176363 bytes
dc.format.extent1038435 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subjectimmutability
dc.subjectmutability
dc.subjectside effect analysis
dc.subjectpurity
dc.subjectpointer analysis
dc.subjectdynamic analysis
dc.subjectmutation
dc.titleCombined static and dynamic mutability analysis


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