7,891 research outputs found
Enhancing Reuse of Constraint Solutions to Improve Symbolic Execution
Constraint solution reuse is an effective approach to save the time of
constraint solving in symbolic execution. Most of the existing reuse approaches
are based on syntactic or semantic equivalence of constraints; e.g. the Green
framework is able to reuse constraints which have different representations but
are semantically equivalent, through canonizing constraints into syntactically
equivalent normal forms. However, syntactic/semantic equivalence is not a
necessary condition for reuse--some constraints are not syntactically or
semantically equivalent, but their solutions still have potential for reuse.
Existing approaches are unable to recognize and reuse such constraints.
In this paper, we present GreenTrie, an extension to the Green framework,
which supports constraint reuse based on the logical implication relations
among constraints. GreenTrie provides a component, called L-Trie, which stores
constraints and solutions into tries, indexed by an implication partial order
graph of constraints. L-Trie is able to carry out logical reduction and logical
subset and superset querying for given constraints, to check for reuse of
previously solved constraints. We report the results of an experimental
assessment of GreenTrie against the original Green framework, which shows that
our extension achieves better reuse of constraint solving result and saves
significant symbolic execution time.Comment: this paper has been submitted to conference ISSTA 201
Automatic Test Generation for Space
The European Space Agency (ESA) uses an engine to perform tests in the Ground
Segment infrastructure, specially the Operational Simulator. This engine uses
many different tools to ensure the development of regression testing
infrastructure and these tests perform black-box testing to the C++ simulator
implementation. VST (VisionSpace Technologies) is one of the companies that
provides these services to ESA and they need a tool to infer automatically
tests from the existing C++ code, instead of writing manually scripts to
perform tests. With this motivation in mind, this paper explores automatic
testing approaches and tools in order to propose a system that satisfies VST
needs
A Novel SAT-Based Approach to the Task Graph Cost-Optimal Scheduling Problem
The Task Graph Cost-Optimal Scheduling Problem consists in scheduling a certain number of interdependent tasks onto a set of heterogeneous processors (characterized by idle and running rates per time unit), minimizing the cost of the entire process. This paper provides a novel formulation for this scheduling puzzle, in which an optimal solution is computed through a sequence of Binate Covering Problems, hinged within a Bounded Model Checking paradigm. In this approach, each covering instance, providing a min-cost trace for a given schedule depth, can be solved with several strategies, resorting to Minimum-Cost Satisfiability solvers or Pseudo-Boolean Optimization tools. Unfortunately, all direct resolution methods show very low efficiency and scalability. As a consequence, we introduce a specialized method to solve the same sequence of problems, based on a traditional all-solution SAT solver. This approach follows the "circuit cofactoring" strategy, as it exploits a powerful technique to capture a large set of solutions for any new SAT counter-example. The overall method is completed with a branch-and-bound heuristic which evaluates lower and upper bounds of the schedule length, to reduce the state space that has to be visited. Our results show that the proposed strategy significantly improves the blind binate covering schema, and it outperforms general purpose state-of-the-art tool
An empirical investigation into branch coverage for C programs using CUTE and AUSTIN
Automated test data generation has remained a topic of considerable interest for several decades because it lies at the heart of attempts to automate the process of Software Testing. This paper reports the results of an empirical study using the dynamic symbolic-execution tool. CUTE, and a search based tool, AUSTIN on five non-trivial open source applications. The aim is to provide practitioners with an assessment of what can be achieved by existing techniques with little or no specialist knowledge and to provide researchers with baseline data against which to measure subsequent work. To achieve this, each tool is applied 'as is', with neither additional tuning nor supporting harnesses and with no adjustments applied to the subject programs under test. The mere fact that these tools can be applied 'out of the box' in this manner reflects the growing maturity of Automated test data generation. However, as might be expected, the study reveals opportunities for improvement and suggests ways to hybridize these two approaches that have hitherto been developed entirely independently. (C) 2010 Elsevier Inc. All rights reserved
Accelerating array constraints in symbolic execution
Despite significant recent advances, the effectiveness of symbolic execution is limited when used to test complex, real-world software. One of the main scalability challenges is related to constraint solv- ing: large applications and long exploration paths lead to complex constraints, often involving big arrays indexed by symbolic expres- sions. In this paper, we propose a set of semantics-preserving trans- formations for array operations that take advantage of contextual information collected during symbolic execution. Our transforma- tions lead to simpler encodings and hence better performance in constraint solving. The results we obtain are encouraging: we show, through an extensive experimental analysis, that our transforma- tions help to significantly improve the performance of symbolic execution in the presence of arrays. We also show that our transfor- mations enable the analysis of new code, which would be otherwise out of reach for symbolic execution
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