Data-Driven context-Sensitivity for points-to analysis

Sehun Jeong, Minseok Jeon, Sungdeok Cha, Hakjoo Oh

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

We present a new data-driven approach to achieve highly cost-effective context-sensitive points-to analysis for Java. While context-sensitivity has greater impact on the analysis precision and performance than any other precision-improving techniques, it is difficult to accurately identify the methods that would benefit the most from context-sensitivity and decide how much context-sensitivity should be used for them. Manually designing such rules is a nontrivial and laborious task that often delivers suboptimal results in practice. To overcome these challenges, we propose an automated and data-driven approach that learns to effectively apply context-sensitivity from codebases. In our approach, points-to analysis is equipped with a parameterized and heuristic rules, in disjunctive form of properties on program elements, that decide when and how much to apply context-sensitivity. We present a greedy algorithm that efficiently learns the parameter of the heuristic rules. We implemented our approach in the Doop framework and evaluated using three types of context-sensitive analyses: conventional object-sensitivity, selective hybrid object-sensitivity, and type-sensitivity. In all cases, experimental results show that our approach significantly outperforms existing techniques.

Original languageEnglish
Article number100
JournalProceedings of the ACM on Programming Languages
Volume1
Issue numberOOPSLA
DOIs
Publication statusPublished - 2017 Oct

Keywords

  • Context-sensitivity
  • Data-driven program analysis
  • Points-to analysis

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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