Symbolic bounds analysis of pointers, array indices, and accessed memory regions

RuginaRadu,; RinardMartin,

doi:10.1145/358438.349325

Cited by 57 publications

(68 citation statements)

References 32 publications

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“…It computes ranges for values of integer valued variables and for pointers under the hypothesis that runs have bounded length. It is based on the framework presented in [40]. Our technique produces tighter upper bounds because it does not compute feasible intervals for variables, but instead checks each individual value.…”

Section: Related Workmentioning

confidence: 99%

TACO: Efficient SAT-Based Bounded Verification Using Symmetry Breaking and Tight Bounds

Galeotti

Rosner

Pombo

et al. 2013

IIEEE Trans. Software Eng.

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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.

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Section: Related Workmentioning

confidence: 99%

TACO: Efficient SAT-Based Bounded Verification Using Symmetry Breaking and Tight Bounds

Galeotti

Rosner

Pombo

et al. 2013

IIEEE Trans. Software Eng.

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“…Cousot [24] proposed an early method for discovering the linear relationships between variables. Rugina et al [25] employ symbolic constraint solvers to determine symbolic bounds of each variable. Symbolic analysis has been used extensively to support the detection of parallelism [4].…”

Section: Related Workmentioning

confidence: 99%

An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables

Anand

ElWazeer

Kotha

et al. 2013

2013 IEEE International Conference on Software Maintenance

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This paper makes two contributions regarding reverse engineering of executables. First, techniques are presented for recovering a precise and correct stack memory model in executables in presence of executable-specific artifacts such as indirect control transfers. Next, the enhanced memory model is employed to define a novel symbolic analysis framework for executables that can perform the same types of program analysis as source-level tools. Frameworks hitherto fail to simultaneously maintain the properties of correct representation and precise memory model and ignore memory-allocated variables while defining symbolic analysis mechanisms. Our methods do not use symbolic, relocation, or debug information, which are usually absent in deployed binaries. We describe our framework, highlighting the novel intellectual contributions of our approach, and demonstrate its efficacy and robustness by applying it to various traditional analyses, including identifying information flow vulnerabilities in five real-world programs.

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“…There have been several static array-based data dependence testing techniques proposed in the literature [3,8,9,11,12,13,14,17,19,22,24,26,28,29]. Unfortunately, not all of these techniques have had been implemented and fully evaluated.…”

Section: Related Workmentioning

confidence: 99%

Discovering Maximum Parallelization Using Advanced Data Dependence Analysis

Birch

Psarris

2008

2008 10th IEEE International Conference on High Performance Computing and Communications

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Accurate data dependence testing allows a compiler to perform safe automatic code optimization and parallelization. It has been shown that factors, such as loop variants and nonlinear expressions, limit opportunity for data dependence testing and parallelization. Recently, the NLVITest has been introduced as new technology to enable exact data dependence testing on nonlinear expressions. Apart from this work, analyses that utilize the Chains of Recurrences formalism have been shown to boost a dependence test's ability to analyze expressions. In this paper we present techniques for applying the NLVI-Test ideas in conjunction with Chains of Recurrences analysis, to couple the benefits of both. In addition, we develop a "Parallelization Index" which describes the upper bound of the total parallelization obtainable for our compiler infrastructure. We perform an experimental evaluation of our efforts on scientific benchmarks. Results show our techniques result in higher numbers of total parallel loops discovered, and moreover, that we consistently expose a majority of the parallelism obtainable.

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Symbolic bounds analysis of pointers, array indices, and accessed memory regions

Cited by 57 publications

References 32 publications

TACO: Efficient SAT-Based Bounded Verification Using Symmetry Breaking and Tight Bounds

TACO: Efficient SAT-Based Bounded Verification Using Symmetry Breaking and Tight Bounds

An Accurate Stack Memory Abstraction and Symbolic Analysis Framework for Executables

Discovering Maximum Parallelization Using Advanced Data Dependence Analysis

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