Memoise: a tool for memoized symbolic execution

Authors:
Guowei Yang;Sarfraz Khurshid;Corina S. Păsăreanu
Affiliations:
University of Texas at Austin, USA;University of Texas at Austin, USA;Carnegie Mellon Silicon Valley, USA / NASA Ames Research Center, USA
Venue:
Proceedings of the 2013 International Conference on Software Engineering
Year:
2013

Citing 11
Cited 1

New trie data structures which support very fast search operations

Journal of Computer and System Sciences
Symbolic execution and program testing

Communications of the ACM
A program testing system

ACM '76 Proceedings of the 1976 annual conference
DART: directed automated random testing

Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Combining symbolic execution with model checking to verify parallel numerical programs

ACM Transactions on Software Engineering and Methodology (TOSEM)
KLEE: unassisted and automatic generation of high-coverage tests for complex systems programs

OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Symbolic PathFinder: symbolic execution of Java bytecode

Proceedings of the IEEE/ACM international conference on Automated software engineering
Directed incremental symbolic execution

Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
Polyglot: modeling and analysis for multiple Statechart formalisms

Proceedings of the 2011 International Symposium on Software Testing and Analysis
CUTE and jCUTE: concolic unit testing and explicit path model-checking tools

CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
Memoized symbolic execution

Proceedings of the 2012 International Symposium on Software Testing and Analysis

Symbolic PathFinder v7

ACM SIGSOFT Software Engineering Notes

Quantified Score

Hi-index	0.00

Visualization

Abstract

This tool paper presents a tool for performing memoized symbolic execution (Memoise), an approach we developed in previous work for more efficient application of symbolic execution. The key idea in Memoise is to allow re-use of symbolic execution results across different runs of symbolic execution without having to re-compute previously computed results as done in earlier approaches. Specifically, Memoise builds a trie-based data structure to record path exploration information during a run of symbolic execution, optimizes the trie for the next run, and re-uses the resulting trie during the next run. Our tool optimizes symbolic execution in three standard scenarios where it is commonly applied: iterative deepening, regression analysis, and heuristic search. Our tool Memoise builds on the Symbolic PathFinder framework to provide more efficient symbolic execution of Java programs and is available online for download. The tool demonstration video is available at http://www.youtube.com/watch?v=ppfYOB0Z2vY.