Abstract Semantic Dependency

Cousot, Patrick

doi:10.1007/978-3-030-32304-2_19

Cited by 12 publications

(8 citation statements)

References 65 publications

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“…In a recent publication, Cousot [2019] presents an analysis that is very similar to ours, especially with respect to the flow-sensitivity of uniformity. He too uses inductively defined syntax which simplifies certain aspects of the analysis as laid out above.…”

Section: Related Workmentioning

confidence: 72%

An abstract interpretation for SPMD divergence on reducible control flow graphs

Rosemann

Moll

Hack

2021

Proc. ACM Program. Lang.

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Vectorizing compilers employ divergence analysis to detect at which program point a specific variable is uniform, i.e. has the same value on all SPMD threads that execute this program point. They exploit uniformity to retain branching to counter branch divergence and defer computations to scalar processor units. Divergence is a hyper-property and is closely related to non-interference and binding time. There exist several divergence, binding time, and non-interference analyses already but they either sacrifice precision or make significant restrictions to the syntactical structure of the program in order to achieve soundness. In this paper, we present the first abstract interpretation for uniformity that is general enough to be applicable to reducible CFGs and, at the same time, more precise than other analyses that achieve at least the same generality. Our analysis comes with a correctness proof that is to a large part mechanized in Coq. Our experimental evaluation shows that the compile time and the precision of our analysis is on par with LLVM's default divergence analysis that is only sound on more restricted CFGs. At the same time, our analysis is faster and achieves better precision than a state-of-the-art non-interference analysis that is sound and at least as general as our analysis.

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Section: Related Workmentioning

confidence: 72%

An abstract interpretation for SPMD divergence on reducible control flow graphs

Rosemann

Moll

Hack

2021

Proc. ACM Program. Lang.

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“…This paper bridges the gap between classic safety properties analysis and security hyperproperties [21] in order to rule out the existence of exploitable runtime errors from a software system. Our approach is closely related to [24], which proposes a technique that can prove noninterference using the abstract interpretation framework. Nevertheless, our technique supports features such as nondeterminism and dynamic user input that are not considered in [24].…”

Section: Motivationmentioning

confidence: 99%

“…Our approach is closely related to [24], which proposes a technique that can prove noninterference using the abstract interpretation framework. Nevertheless, our technique supports features such as nondeterminism and dynamic user input that are not considered in [24]. Furthermore, we leverage the values of the variables to enhance the precision of our analysis, which is an extension proposed but not implemented in [24].…”

Section: Motivationmentioning

confidence: 99%

Sound Abstract Nonexploitability Analysis

Parolini,

Miné

2023

Lecture Notes in Computer Science

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Unifying Theory of Formal Methods. As a general theory of semantic approximation, abstract interpretation is not only useful to derive new static analyses, but can also model other static analysis methods developed independently, such as data-flow analyses [42] or security analyses [43]. It can also reason about other formal techniques, such as strong typing [39], constraint programming [123], as well as model checking [41] and SMT solving [18].…”

Section: Abstract Interpretationmentioning

confidence: 99%

A Review of Formal Methods applied to Machine Learning

Urban¹,

Miné²

2021

Preprint

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We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability.We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems.

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Abstract Semantic Dependency

Cited by 12 publications

References 65 publications

An abstract interpretation for SPMD divergence on reducible control flow graphs

An abstract interpretation for SPMD divergence on reducible control flow graphs

Sound Abstract Nonexploitability Analysis

A Review of Formal Methods applied to Machine Learning

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