Improving Error Discovery Using Guided Model Checking Improving Error Discovery Using Guided Model Checking

by Rungta, Neha Shyam

Abstract (Summary)
State exploration in directed software model checking is guided using a heuristic function to move states near errors to the front of the search queue. Distance heuristic functions rank states based on the number of transitions needed to move the current program state into an error location. Lack of calling context information causes the heuristic function to underestimate the true distance to the error; however, inlining functions at call sites in the control flow graph to capture calling context leads to exponential growth in the computation. This paper presents a new algorithm that implicitly inlines functions at call sites to compute distance data with unbounded calling context that is polynomial in the number of nodes in the control flow graph. The new algorithm propagates distance data through call sites during a depth-first traversal of the program. We show in a series of benchmark examples that the new heuristic function with unbounded distance data is more efficient than the same heuristic function that inlines functions up to a certain depth.
Bibliographical Information:


School:Brigham Young University

School Location:USA - Utah

Source Type:Master's Thesis

Keywords:software verification model checking guided heuristics


Date of Publication:09/11/2006

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