Test input generation using dynamic programming

  • Authors:

  • Razieh Nokhbeh Zaeem;Sarfraz Khurshid

  • Affiliations:

  • The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX

  • Venue:
  • Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering
  • Year:
  • 2012

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Abstract

Constraint-based input generation is an effective technique for testing programs, such as compilers and web browsers, which have complex inputs. However, efficient generation of such inputs remains a challenging problem. We present a novel input generation technique that takes constraints written as recursive predicates in the underlying programming language and uses dynamic programming to solve the constraints efficiently. Our key insight is to leverage the recursive structure of desired inputs and partition the problem of generating an input into several sub-problems of generating smaller inputs that exhibit the same structure, and then to use dynamic programming -- a well-known problem solving methodology designed to exploit common sub-problems -- to combine them. A lazy initialization strategy and symbolic execution optimize our basic technique. Our technique provides not only bounded exhaustive input generation but also enables random input generation. We show the correctness of our technique. Furthermore, we present an experimental evaluation, which shows that our technique can provide over an order of magnitude performance improvement for input generation compared to Korat (an efficient solver for structural constraints) and Pex (a state-of-the-art tool for symbolic execution). Finally, we use our technique to effectively find bugs in production versions of Google Chrome and Apple Safari web browsers.