Using dynamic analysis to generate disjunctive invariants

Thanh Vu Nguyen, Deepak Kapur, Westley Weimer, Stephanie Forrest

Research output: Contribution to journalConference article

20 Scopus citations

Abstract

Program invariants are important for defect detection, program verification, and program repair. However, existing techniques have limited support for important classes of invariants such as disjunctions, which express the semantics of conditional statements. We propose a method for generating disjunctive invariants over numerical domains, which are inexpressible using classical convex polyhedra. Using dynamic analysis and reformulating the problem in non-standard ''max-plus'' and ''min-plus'' algebras, our method constructs hulls over program trace points. Critically, we introduce and infer a weak class of such invariants that balances expressive power against the computational cost of generating nonconvex shapes in high dimensions. Existing dynamic inference techniques often generate spurious invariants that fit some program traces but do not generalize. With the insight that generating dynamic invariants is easy, we propose to verify these invariants statically using k-inductive SMT theorem proving which allows us to validate invariants that are not classically inductive. Results on difficult kernels involving nonlinear arithmetic and abstract arrays suggest that this hybrid approach efficiently generates and proves correct program invariants.

Original languageEnglish (US)
Pages (from-to)608-619
Number of pages12
JournalProceedings - International Conference on Software Engineering
Issue number1
DOIs
StatePublished - May 31 2014
Event36th International Conference on Software Engineering, ICSE 2014 - Hyderabad, India
Duration: May 31 2014Jun 7 2014

Keywords

  • Program analysis
  • disjunctive invariants
  • invariant generation
  • static and dynamic analyses
  • theorem proving

ASJC Scopus subject areas

  • Software

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