Selective context-sensitivity guided by impact pre-analysis

Hakjoo Oh, Wonchan Lee, Kihong Heo, Hongseok Yang, Kwangkeun Yi

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


We present a method for selectively applying context-sensitivity during interprocedural program analysis. Our method applies context-sensitivity only when and where doing so is likely to improve the precision that matters for resolving given queries. The idea is to use a pre-analysis to estimate the impact of contextsensitivity on the main analysis's precision, and to use this information to find out when and where the main analysis should turn on or off its context-sensitivity. We formalize this approach and prove that the analysis always benefits from the pre-analysisguided context-sensitivity. We implemented this selective method for an existing industrial-strength interval analyzer for full C. The method reduced the number of (false) alarms by 24.4%, while increasing the analysis cost by 27.8% on average.

The use of the selective method is not limited to contextsensitivity. We demonstrate this generality by following the same principle and developing a selective relational analysis. Categories and Subject Descriptors F.3.2 [Semantics of Programming Languages]: Program Analysis

Original languageEnglish
Pages (from-to)475-484
Number of pages10
JournalACM SIGPLAN Notices
Issue number6
Publication statusPublished - 2014 Jun 5
Externally publishedYes


  • Context-sensitive analysis
  • Static analysis

ASJC Scopus subject areas

  • General Computer Science


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