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dc.contributor.authorOrso, Alessandroen_US
dc.contributor.authorLiang, Donglin
dc.contributor.authorSinha, Saurabh
dc.contributor.authorHarrold, Mary Jean
dc.date.accessioned2005-06-17T17:39:26Z
dc.date.available2005-06-17T17:39:26Z
dc.date.issued2002en_US
dc.identifier.urihttp://hdl.handle.net/1853/6529
dc.description.abstractIdentifying and understanding data dependences is important for a variety of software-engineering tasks. The presence of pointers, arrays, and dynamic memory allocation introduces subtle and complex data dependences that may be difficult to understand. In this paper, we present a refinement of our previously developed classification that also distinguishes the types of memory locations, considers interprocedural data dependences, and further distinguishes such data dependences based on the kinds of interprocedura paths on which they occur. This new classification enables reasoning about the complexity of data dependences in programs using features such as pointers, arrays, and dynamic memory allocation. We present an algorithm for computing interprocedural data dependences according to our classification. To evaluate the classification, we compute the distribution of data dependences for a set of real C programs and we discuss how the distribution can be useful in understanding the characteristics of a program. We also evaluate how alias information provided by different algorithms, varying in precision, affects the distribution. Finally, we investigate how the classification can be exploited to estimate complexity of the data dependences in a program.en_US
dc.format.extent417659 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCC Technical Report; GIT-CC-02-13en_US
dc.subjectData dependences
dc.subjectData flow
dc.subjectStatic analysis
dc.subjectPointers
dc.titleA Framework for Understanding Data Dependencesen_US
dc.typeTechnical Reporteng_US


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