Saturn: A scalable framework for error detection using Boolean satisfiability

  • Authors:

  • Yichen Xie;Alex Aiken

  • Affiliations:

  • Stanford University, Stanford, CA;Stanford University, Stanford, CA

  • Venue:
  • ACM Transactions on Programming Languages and Systems (TOPLAS) - Special issue on POPL 2005
  • Year:
  • 2007

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Abstract

This article presents Saturn, a general framework for building precise and scalable static error detection systems. Saturn exploits recent advances in Boolean satisfiability (SAT) solvers and is path sensitive, precise down to the bit level, and models pointers and heap data. Our approach is also highly scalable, which we achieve using two techniques. First, for each program function, several optimizations compress the size of the Boolean formulas that model the control flow and data flow and the heap locations accessed by a function. Second, summaries in the spirit of type signatures are computed for each function, allowing interprocedural analysis without a dramatic increase in the size of the Boolean constraints to be solved. We have experimentally validated our approach by conducting two case studies involving a Linux lock checker and a memory leak checker. Results from the experiments show that our system scales well, parallelizes well, and finds more errors with fewer false positives than previous static error detection systems.