ComplexityParser: An Automatic Tool for Certifying Poly-Time Complexity of Java Programs

Hainry, Emmanuel; Jeandel, Emmanuel; Péchoux, Romain; Zeyen, Olivier

doi:10.1007/978-3-030-85315-0_20

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…Several tools that evaluate resource bounds already exist [1,2,8,12,13,14,15,17]; including LOOPUS [25] and C4B [9], that specialize in C language inputs. Comprehensive evaluations of these tools have also been performed recently [9,11,25].…”

Section: Originalities Of Mwp-flow Analysismentioning

confidence: 99%

“…Certification of any program is incomplete if it ignores resource considerations, as runtime failure will occur if usage exceeds available capacity. To address this deficiency, automatic complexity analysis produced many different implementations [9,13,14,15] with varying features. This paper presents the development and specificities of our automatic static complexity analyzer, pymwp.…”

Section: Introduction -Making Use Of Implicit Complexitymentioning

confidence: 99%

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pymwp: A Static Analyzer Determining Polynomial Growth Bounds

Aubert,

Rubiano,

Rusch

et al. 2023

Lecture Notes in Computer Science

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We present pymwp, a static analyzer that automatically computes, if they exist, polynomial bounds relating input and output sizes. In case of exponential growth, our tool detects precisely which dependencies between variables induced it. Based on the sound mwp-flow calculus, the analysis captures bounds on large classes of programs by being non-deterministic and not requiring termination. For this reason, implementing this calculus required solving several non-trivial implementation problems, to handle its complexity and non-determinism, but also to provide meaningful feedback to the programmer. The duality of the analysis result and compositionality of the calculus make our approach original in the landscape of complexity analyzers. We conclude by demonstrating experimentally how pymwp is a practical and performant static analyzer to automatically evaluate variable growth bounds of C programs.

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Section: Originalities Of Mwp-flow Analysismentioning

confidence: 99%

Section: Introduction -Making Use Of Implicit Complexitymentioning

confidence: 99%

pymwp: A Static Analyzer Determining Polynomial Growth Bounds

Aubert,

Rubiano,

Rusch

et al. 2023

Lecture Notes in Computer Science

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show abstract

“…Other communities share a similar goal of inferring resource-usage. Complexity analyzers such as SPEED [14] for C++, COSTA [1] for Java bytecode, ComplexityParser [16] for Java, Resource Aware ML for OCaml [24] or Cerco [2] and Verasco [20] for C generate (certified) cost or runtime analysis on (subsets of) imperative programming languages. Embracing such a large diversity is difficult, but our technique is different from existing implementations and tools: most of them focus on worst-case resource-usage complexity or termination, while we are interested in upper-bounds on the final values of program variables, i.e.…”

Section: :15mentioning

confidence: 99%

mwp-Analysis Improvement and Implementation: Realizing Implicit Computational Complexity

Aubert¹,

Rubiano²,

Rusch³

et al. 2022

Preprint

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Implicit Computational Complexity (ICC) drives better understanding of complexity classes, but it also guides the development of resources-aware languages and static source code analyzers. Among the methods developed, the mwp-flow analysis [18] certifies polynomial bounds on the size of the values manipulated by an imperative program. This result is obtained by bounding the transitions between states instead of focusing on states in isolation, as most static analyzers do, and is not concerned with termination or tight bounds on values. Those differences, along with its built-in compositionality, make the mwp-flow analysis a good target for determining how ICC-inspired techniques diverge compared with more traditional static analysis methods. This paper's contributions are three-fold: we fine-tune the internal machinery of the original analysis to make it tractable in practice; we extend the analysis to function calls and leverage its machinery to compute the result of the analysis efficiently; and we implement the resulting analysis as a lightweight tool to automatically perform data-size analysis of C programs. This documented effort prepares and enables the development of certified complexity analysis, by transforming a costly analysis into a tractable program, that furthermore decorrelates the problem of deciding if a bound exist with the problem of computing it.

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Formally Verified Resource Bounds through Implicit Computational Complexity

Rusch

2022

Companion Proceedings of the 2022 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: So

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ComplexityParser: An Automatic Tool for Certifying Poly-Time Complexity of Java Programs

Cited by 4 publications

References 33 publications

pymwp: A Static Analyzer Determining Polynomial Growth Bounds

pymwp: A Static Analyzer Determining Polynomial Growth Bounds

mwp-Analysis Improvement and Implementation: Realizing Implicit Computational Complexity

Formally Verified Resource Bounds through Implicit Computational Complexity

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