Learning-Based Assume-Guarantee Regression Verification

He, Fei; Shu, Mao; Wang, Bow-Yaw

doi:10.1007/978-3-319-41528-4_17

Cited by 10 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They implemented their technique in LTSA tool and applied successfully to NASA system. Model learning was employed in another work performed by He et al [185], where the authors presented a learning-based assume-guarantee regression verification technique. In model checking, much internal information is computed during the running process of verification.…”

Section: Compositional Reasoning/verificationmentioning

confidence: 99%

“…For example, when one revision is completed, then the internal information computed by model checking may still be useful for the verification of the next revision. He et al [185] fully utilized this idea in their technique and they reuse the contextual assumptions of the previous round of verification in the current round. Similarly, there are some other case studies where model learning algorithms were utilized for learning assumptions for compositional verification automatically [186,187,188].…”

Section: Compositional Reasoning/verificationmentioning

confidence: 99%

See 1 more Smart Citation

Model Learning: A Survey on Foundation, Tools and Applications

Ali,

Sun,

Zhao

2019

Preprint

View full text Add to dashboard Cite

The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to enhance the reliability of software systems. Formal models form the basis and are prerequisite for the application of these techniques. An emerging and promising model learning technique can complement testing and verification techniques by providing learned models of black box systems fully automatically. This paper surveys one such state of the art technique called model learning which recently has attracted much attention of researchers especially from the domains of testing and verification. This survey paper reviews and provides comparison summaries highlighting the merits and shortcomings of learning techniques, algorithms, and tools which form the basis of model learning. This paper also surveys the successful applications of model learning technique in multidisciplinary fields making it promising for testing and verification of realistic systems.

show abstract

Section: Compositional Reasoning/verificationmentioning

confidence: 99%

Section: Compositional Reasoning/verificationmentioning

confidence: 99%

Model Learning: A Survey on Foundation, Tools and Applications

Ali,

Sun,

Zhao

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The assumptions have been traditionally generated by users which strongly limited the assume-guarantee reasoning practically. Some proposals have been proposed to develop the assumptions automatically such as using learning assumptions [81,52,25].…”

Section: Random Walk Same As Ant Colonymentioning

confidence: 99%

Handling state space explosion in verification of component-based systems: A review

Nejati,

Ghani,

Yap

et al. 2017

Preprint

View full text Add to dashboard Cite

Component-based design is a different way of constructing systems which offers numerous benefits, in particular, decreasing the complexity of system design. However, deploying components into a system is a challenging and error-prone task. Model checking is one of the reliable methods that automatically and systematically analyses the correctness of a given system. Its brute-force check of the state space significantly expands the level of confidence in the system. Nevertheless, model checking is limited by a critical problem so-called Sate Space Explosion (SSE). To benefit from model checking, appropriate methods to reduce SSE, is required. In two last decades, a great number of methods to mitigate the state space explosion have been proposed which have many similarities, dissimilarities, and unclear concepts in some cases. This research, firstly, aims at present a review and brief discussion of the methods of handling SSE problem and classify them based on their similarities, principle and characteristics. Second, it investigates the methods for handling SSE problem in verifying Component-based system (CBS) and provides insight into CBS verification limitations that have not been addressed yet. The analysis in this research has revealed the patterns, specific features, and gaps in the state-of-the-art methods. In addition, we identified and discussed suitable methods to soften SSE problem in CBS and underlined the key challenges for future research efforts.

show abstract