A sparse evaluation technique for detailed semantic analyses

Yoonseok Ko, Kihong Heo, Hakjoo Oh

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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 14x.

Original languageEnglish
Pages (from-to)99-111
Number of pages13
JournalComputer Languages, Systems and Structures
Issue number3-4
Publication statusPublished - 2014 Oct 1
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the Engineering Research Center of Excellence Program of Korea Ministry of Science, ICT & Future Planning (MSIP)/National Research Foundation of Korea (NRF) (Grant NRF-2008-0062609 ).

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.


  • Abstract interpretation
  • Data-flow analysis
  • Sparse evaluation
  • Static analysis

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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