Christoph Csallner, Nikolai Tillmann, and Yannis Smaragdakis
Dynamically discovering likely program invariants from concrete test executions has emerged as a highly promising software engineering technique. Dynamic invariant inference has the advantage of succinctly summarizing both "expected" program inputs and the subset of program behaviors that is normal under those inputs. In this paper, we introduce a technique that can drastically increase the relevance of inferred invariants, or reduce the size of the test suite required to obtain good invariants. Instead of falsifying invariants produced by pre-set patterns, we determine likely program invariants by combining the concrete execution of actual test cases with a simultaneous symbolic execution of the same tests. The symbolic execution produces abstract conditions over program variables that the concrete tests satisfy during their execution. In this way, we obtain the benefits of dynamic inference tools like Daikon: the inferred invariants correspond to the observed program behaviors. At the same time, however, our inferred invariants are much more suited to the program at hand than Daikon's hard-coded invariant patterns. The symbolic invariants are literally derived from the program text itself, with appropriate value substitutions as dictated by symbolic execution.
We implemented our technique in the DySy tool, which utilizes a powerful symbolic execution and simplification engine. The results confirm the benefits of our approach. In Daikon's prime example benchmark, we infer the majority of the interesting Daikon invariants, while eliminating invariants that a human user is likely to consider irrelevant.
|Published in||ICSE '08: Proceedings of the 30th international conference on Software engineering|
|Address||New York, NY, USA|
|Publisher||Association for Computing Machinery, Inc.|
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or firstname.lastname@example.org. The definitive version of this paper can be found at ACM’s Digital Library --http://www.acm.org/dl/.
Christoph Csallner, Nikolai Tillmann, and Yannis Smaragdakis. DySy: Dynamic Symbolic Execution for Invariant Inference, Microsoft, November 2007.