A sparse evaluation technique for detailed semantic analyses

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We present a sparse evaluation technique that is effectively applicable to a set of elaborate semantic-based static analyses. Existing sparse evaluation techniques are effective only when the underlying analyses have comparably low precision. For example, if a pointer analysis precision is not affected by numeric statements like x:=1 then existing sparse evaluation techniques can remove the statement, but otherwise, the statement cannot be removed. Our technique, which is a fine-grained sparse evaluation technique, is effectively applicable even to elaborate analyses. A key insight of our technique is that, even though a statement is relevant to an analysis, it is typical that analyzing the statement involves only a tiny subset of its input abstract memory and the most are irrelevant. By exploiting this sparsity, our technique transforms the original analysis into a form that does not involve the fine-grained irrelevant semantic behaviors. We formalize our technique within the abstract interpretation framework. In experiments with a C static analyzer, our technique improved the analysis speed by on average 14 x.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2014-10
Language
English
Article Type
Article
Citation

COMPUTER LANGUAGES SYSTEMS & STRUCTURES, v.40, no.3-4, pp.99 - 111

ISSN
1477-8424
DOI
10.1016/j.cl.2014.05.001
URI
http://hdl.handle.net/10203/201081
Appears in Collection
CS-Journal Papers(저널논문)
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