Counterexample Driven Refinement for Abstract Interpretation

  • Bhargav S. Gulavani
  • Sriram K. Rajamani
Conference paper

DOI: 10.1007/11691372_34

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3920)
Cite this paper as:
Gulavani B.S., Rajamani S.K. (2006) Counterexample Driven Refinement for Abstract Interpretation. In: Hermanns H., Palsberg J. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2006. Lecture Notes in Computer Science, vol 3920. Springer, Berlin, Heidelberg

Abstract

Abstract interpretation techniques prove properties of programs by computing abstract fixpoints. All such analyses suffer from the possibility of false errors. We present a new counterexample driven refinement technique to reduce false errors in abstract interpretations. Our technique keeps track of the precision losses during forward fixpoint computation, and does a precise backward propagation from the error to either confirm the error as a true error, or identify a refinement so as to avoid the false error.

Our technique is quite simple, and is independent of the specific abstract domain used. An implementation of our technique for affine transition systems is able to prove invariants generated by the StInG tool [19] without doing any specialized analysis for linear relations. Thus, we hope that the technique can work for other abstract domains as well. We sketch how our technique can be used to perform shape analysis by simply defining an appropriate widening operator over shape graphs.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bhargav S. Gulavani
    • 1
  • Sriram K. Rajamani
    • 2
  1. 1.IIT BombayIndia
  2. 2.Microsoft ResearchIndia

Personalised recommendations