SemSlice: Exploiting Relational Verification for Automatic Program Slicing

Beckert, Bernhard; Bormer, Thorsten; Gocht, Stephan; Herda, Mihai; Lentzsch, Daniel; Ulbrich, Mattias

doi:10.1007/978-3-319-66845-1_20

Cited by 5 publications

(9 citation statements)

References 16 publications

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“…We start the discussion by reiterating the evaluation results of the implementation of the framework consisting of the tool SemSlice [5], as shown in Table 1. For the evaluation, we used a collection of small but intricate examples (e.g., the example of Fig.…”

Section: Discussionmentioning

confidence: 99%

“…A solution for dealing with language complexity is to perform the analysis on a simpler, intermediate representation. While the implementation of our slicing framework [5] works on LLVM IR [1] programs, to keep the definitions in this paper easy to understand, we here use a language whose computational model is similar to that of LLVM IR but that has only four instructions: skip, halt, assign, and jnz . We formalize the notions of slice candidate, slicing criterion and valid slice using a computation model based on a register machine with an unbounded number of registers.…”

Section: Static Backward Slicingmentioning

confidence: 99%

“…The feasibility of our framework has been shown in a tool paper [5] describing an implementation. Here, we focus on the theoretical background of the framework.…”

Section: Introductionmentioning

confidence: 99%

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Using Relational Verification for Program Slicing

Beckert

Bormer

Gocht

et al. 2019

Software Engineering and Formal Methods

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Program slicing is the process of removing statements from a program such that defined aspects of its behavior are retained. For producing precise slices, i.e., slices that are minimal in size, the program's semantics must be considered. Existing approaches that go beyond a syntactical analysis and do take the semantics into account are not fully automatic and require auxiliary specifications from the user. In this paper, we adapt relational verification to check whether a slice candidate obtained by removing some instructions from a program is indeed a valid slice. Based on this, we propose a framework for precise and automatic program slicing. As part of this framework, we present three strategies for the generation of slice candidates, and we show how dynamic slicing approaches-that interweave generating and checking slice candidatescan be used for this purpose. The framework can easily be extended with other strategies for generating slice candidates. We discuss the strengths and weaknesses of slicing approaches that use our framework.

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Section: Discussionmentioning

confidence: 99%

Section: Static Backward Slicingmentioning

confidence: 99%

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Using Relational Verification for Program Slicing

Beckert

Bormer

Gocht

et al. 2019

Software Engineering and Formal Methods

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“…Relational properties are useful when reasoning about program refinement, approximation, equivalence, provenance, as well as many notions of security. A great many relational program analyses have been proposed in the recent literature, including works by Antonopoulos et al (2017); Asada et al (2016); Banerjee et al (2016); Barthe et al (2012Barthe et al ( , 2013bBarthe et al ( , 2014Barthe et al ( , 2015; Beckert et al ( , 2017; Benton et al (2009); Ştefan ; Godlin and Strichman (2010); Hedin and Sabelfeld (2012); Kundu et al (2009); Küsters et al (2015); Yang (2007); Zaks and Pnueli (2008); Murray et al (2013); Fehrenbach and Cheney (2016); Bauereiß et al (2016Bauereiß et al ( , 2017; and Çiçek et al (2017). While some systems have been designed for the efficient verification of specialized relational properties of programs (notably information-flow type systems, e.g., Sabelfeld and Myers (2003a)), others support larger classes of properties.…”

Section: Introductionmentioning

confidence: 99%

A monadic framework for relational verification: applied to information security, program equivalence, and optimizations

Grimm¹,

Maillard²,

Fournet³

et al. 2018

Proceedings of the 7th ACM SIGPLAN International Conference on Certified Programs and Proofs - CPP 2018

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Relational properties describe multiple runs of one or more programs. They characterize many useful notions of security, program refinement, and equivalence for programs with diverse computational effects, and they have received much attention in the recent literature. Rather than developing separate tools for special classes of effects and relational properties, we advocate using a general purpose proof assistant as a unifying framework for the relational verification of effectful programs. The essence of our approach is to model effectful computations using monads and to prove relational properties on their monadic representations, making the most of existing support for reasoning about pure programs.We apply this method in F ⋆ and evaluate it by encoding a variety of relational program analyses, including information flow control, semantic declassification, program equivalence and refinement at higher order, correctness of program optimizations and game-based cryptographic security. By relying on SMT-based automation, unary weakest preconditions, user-defined effects, and monadic reification, we show that, compared to unary properties, verifying relational properties requires little additional effort from the F ⋆ programmer.

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“…Trust and accountability requirements for a smart contract can be expressed through a combination of authority algebra and FOL [87]. Correctness of smart contracts is also successfully verified using a dynamic logic developed for Java programs [14,48,49]. Last but not least, formalisms that are based on deontic logic enable analysis over legal smart contracts, which are challenging to formalize otherwise [172].…”

Section: Program Logicsmentioning

confidence: 99%

Securing smart contracts with formal verification and automated program repair

Tolmach¹

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had provided me with guidance, encouragement, but also a lot of freedom and resources to explore possible research directions for me to find those I was particulary excited about. I also learned a lot from him, as well as his never-ending energy, enthusiasm, and positive attitude.I also want to thank my co-supervisor Dr Wei Qingsong for his support, encouragement, valuable advice, and help with so many aspects of my PhD. I am immensely indebted to Dr Li Zengxiang who introduced me to academia, research, and blockchain, and helped me start my PhD in Singapore. I am grateful to A*STAR for supporting my PhD, financially and otherwise. I am extremely lucky to have Prof Li Yi and Prof Lin Shang-Wei as my collaborators and mentors, and would like to thank them for their continuous support, insightful research advice, editing my writing, and so much more. Their contributions to this work are invaluable. I would also like to express my appreciation to my wonderful labmates, especially Mr Liu Ye for fruitful research discussions and collaboration. I would also like to extend my gratitude to my friends outside of CSL, particularly Ayana Aspembitova, for their kindness, support, and good example they set for me. I thank my parents, Ihar and Natallia, and my sister Anna for their unparalleled love, endless support, and understanding. Finally, thanks to my husband, Vasily, for being there for me throughout these years. I would have never done it without your support and help. vii Contents Statement of Originality ii

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SemSlice: Exploiting Relational Verification for Automatic Program Slicing

Cited by 5 publications

References 16 publications

Using Relational Verification for Program Slicing

Using Relational Verification for Program Slicing

A monadic framework for relational verification: applied to information security, program equivalence, and optimizations

Securing smart contracts with formal verification and automated program repair

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