Return of CFA: Call-site sensitivity can be superior to object sensitivity even for object-oriented programs

Minseok Jeon, Hakjoo Oh

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we challenge the commonly-accepted wisdom in static analysis that object sensitivity is superior to call-site sensitivity for object-oriented programs. In static analysis of object-oriented programs, object sensitivity has been established as the dominant flavor of context sensitivity thanks to its outstanding precision. On the other hand, call-site sensitivity has been regarded as unsuitable and its use in practice has been constantly discouraged for object-oriented programs. In this paper, however, we claim that call-site sensitivity is generally a superior context abstraction because it is practically possible to transform object sensitivity into more precise call-site sensitivity. Our key insight is that the previously known superiority of object sensitivity holds only in the traditional k-limited setting, where the analysis is enforced to keep the most recent k context elements. However, it no longer holds in a recently-proposed, more general setting with context tunneling. With context tunneling, where the analysis is free to choose an arbitrary k-length subsequence of context strings, we show that call-site sensitivity can simulate object sensitivity almost completely, but not vice versa. To support the claim, we present a technique, called Obj2CFA, for transforming arbitrary context-tunneled object sensitivity into more precise, context-tunneled call-site-sensitivity. We implemented Obj2CFA in Doop and used it to derive a new call-site-sensitive analysis from a state-of-the-art object-sensitive pointer analysis. Experimental results confirm that the resulting call-site sensitivity outperforms object sensitivity in precision and scalability for real-world Java programs. Remarkably, our results show that even 1-call-site sensitivity can be more precise than the conventional 3-object-sensitive analysis.

Original languageEnglish
Article number3498696
JournalProceedings of the ACM on Programming Languages
Volume6
Issue numberPOPL
DOIs
Publication statusPublished - 2022 Jan

Keywords

  • Context sensitivity
  • Machine learning for program analysis
  • Pointer analysis

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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