TACO: efficient SAT-based bounded verification using symmetry breaking and tight bounds

SAT-based bounded verification of annotated code consists of translating the code together with the annotations to a propositional formula, and analyzing the formula for specification violations using a SAT-solver. If a violation is found, an execution trace exposing the failure is exhibited. Code i...

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Detalles Bibliográficos
Autores: Galeotti, Juan Pablo, Rosner, Nicolas Leandro, Lopez Pombo, Carlos Gustavo, Frias, Marcelo Fabian
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2013
País:Argentina
Institución:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/15644
Acceso en línea:http://hdl.handle.net/11336/15644
Access Level:acceso abierto
Palabra clave:Software Engineering
Software Verification
Java Program Analysis
Alloy
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
Descripción
Sumario:SAT-based bounded verification of annotated code consists of translating the code together with the annotations to a propositional formula, and analyzing the formula for specification violations using a SAT-solver. If a violation is found, an execution trace exposing the failure is exhibited. Code involving linked data structures with intricate invariants is particularly hard to analyze using these techniques. In this paper, we present Translation of Annotated COde (TACO), a prototype tool which implements a novel, general, and fully automated technique for the SAT-based analysis of JML-annotated Java sequential programs dealing with complex linked data structures. We instrument code analysis with a symmetry-breaking predicate which, on one hand, reduces the size of the search space by ignoring certain classes of isomorphic models and, on the other hand, allows for the parallel, automated computation of tight bounds for Java fields. Experiments show that the translations to propositional formulas require significantly less propositional variables, leading to an improvement of the efficiency of the analysis of orders of magnitude, compared to the noninstrumented SAT--based analysis. We show that in some cases our tool can uncover bugs that cannot be detected by state-of-the-art tools based on SAT-solving, model checking, or SMT-solving.