Automated Assumption Generation for Compositional Verification

Gupta, Anubhav; McMillan, Kenneth L.; Fu, Zhaohui

doi:10.1007/978-3-540-73368-3_45

Cited by 34 publications

(38 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There have been several attempts to improve performance, including symbolic implementations [31] and optimisations to the use of L* [9]. Others have also devised alternative learning-based methods, for example by reformulating the assumption generation problem as one of computing the smallest finite automaton separating two regular languages [23,11], or using the CDNF learning algorithm to generate implicit representations of assumptions [10].…”

Section: Learning Assumptions For Compositional Verificationmentioning

confidence: 99%

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Kwiatkowska

Parker

2011

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

Abstract. Probabilistic verification techniques have been applied to the formal modelling and analysis of a wide range of systems, from communication protocols such as Bluetooth, to nanoscale computing devices, to biological cellular processes. In order to tackle the inherent challenge of scalability, compositional approaches to verification are sorely needed. An example is assume-guarantee reasoning, where each component of a system is analysed independently, using assumptions about the other components that it interacts with. We discuss recent developments in the area of automated compositional verification techniques for probabilistic systems. In particular, we describe techniques to automatically generate probabilistic assumptions that can be used as the basis for compositional reasoning. We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems. We also present recent improvements and extensions to this work and survey some of the promising potential directions for further research in this area.

show abstract

Section: Learning Assumptions For Compositional Verificationmentioning

confidence: 99%

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Kwiatkowska

Parker

2011

Fundamental Approaches to Software Engineering

View full text Add to dashboard Cite

show abstract

“…4 The reduction was first observed by Gupta et al [14]. 5 The facts that the system violates the property and the two input languages are not disjoint are equivalent to each other, which can be proved as follows: We then choose the largest set from {Q 1 , Q 2 , Q 3 } that contains s 0 as the initial state of the reduced DFA.…”

Section: Heuristics For Efficiencymentioning

confidence: 99%

“…The problem of finding a minimal assumption for compositional verification can be reduced to the problem of finding a minimal separating DFA (deterministic finite automaton) of two disjoint regular languages [14]. A DFA A separates two disjoint languages L 1 and L 2 if its language L(A) contains L 1 and is disjoint from L 2 (L 1 ⊆ L(A) and L(A) ∩ L 2 = ∅).…”

Section: Introductionmentioning

confidence: 99%

“…The DFA A is minimal if it has the least number of states among all separating DFA's. Several approaches [14,16,13] have been proposed to find a minimal separating DFA automatically. However, all of those approaches are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

“…However, all of those approaches are computationally expensive. In particular, the most recent algorithm of Gupta et al [14] has an exponential query complexity in the sizes of the minimal DFA's of the two input languages.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning Minimal Separating DFA’s for Compositional Verification

Chen

Farzan

Clarke

et al. 2009

Tools and Algorithms for the Construction and Analysis of Systems

View full text Add to dashboard Cite

Abstract. Algorithms for learning a minimal separating DFA of two disjoint regular languages have been proposed and adapted for different applications. One of the most important applications is learning minimal contextual assumptions in automated compositional verification. We propose in this paper an efficient learning algorithm, called L Sep , that learns and generates a minimal separating DFA. Our algorithm has a quadratic query complexity in the product of sizes of the minimal DFA's for the two input languages. In contrast, the most recent algorithm of Gupta et al. has an exponential query complexity in the sizes of the two DFA's. Moreover, experimental results show that our learning algorithm significantly outperforms all existing algorithms on randomly-generated example problems. We describe how our algorithm can be adapted for automated compositional verification. The adapted version is evaluated on the LTSA benchmarks and compared with other automated compositional verification approaches. The result shows that our algorithm surpasses others in 30 of 49 benchmark problems.

show abstract

Assume, Guarantee or Repair

Frenkel

Grumberg

Păsăreanu

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We present Assume-Guarantee-Repair (AGR)-a novel framework which not only verifies that a program satisfies a set of properties, but also repairs the program in case the verification fails. We consider communicating programs-these are simple C-like programs, extended with synchronous communication actions over communication channels. Our method, which consists of a learning-based approach to assume-guarantee reasoning, performs verification and repair simultaneously: in every iteration, AGR either makes another step towards proving that the (current) system satisfies the specification, or alters the system in a way that brings it closer to satisfying the specification. We manage handling infinite-state systems by using a finite abstract representation, and reduce the semantic problems in hand-satisfying complex specifications that also contain first-order constraints-to syntactic ones, namely membership and equivalence queries for regular languages. We implemented our algorithm and evaluated it on various examples. Our experiments present compact proofs of correctness and quick repairs.

show abstract

Automated Assumption Generation for Compositional Verification

Cited by 34 publications

References 8 publications

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Learning Minimal Separating DFA’s for Compositional Verification

Assume, Guarantee or Repair

Contact Info

Product

Resources

About