Cyber-physical specification mismatch identification with dynamic analysis

Johnson, Taylor T.; Bak, Stanley; Drager, Steven

doi:10.1145/2735960.2735979

Cited by 12 publications

(6 citation statements)

References 43 publications

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“…CPSDebug originally addresses failure explanation in the context of CPS models. The closest work to CPSDebug is probably Hynger [17,26], which exploits invariant generation to detect mismatches between an actual and an inferred specification in Simulink models. Specification mismatches can indicate the presence of problems in the models.…”

Section: Related Workmentioning

confidence: 99%

CPSDebug: Automatic failure explanation in CPS models

Bartocci

Manjunath

Mariani

et al. 2021

Int J Softw Tools Technol Transfer

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Debugging cyber-physical system (CPS) models is a cumbersome and costly activity. CPS models combine continuous and discrete dynamics—a fault in a physical component manifests itself in a very different way than a fault in a state machine. Furthermore, faults can propagate both in time and space before they can be detected at the observable interface of the model. As a consequence, explaining the reason of an observed failure is challenging and often requires domain-specific knowledge. In this paper, we propose approach, a novel CPSDebug that combines testing, specification mining, and failure analysis, to automatically explain failures in Simulink/Stateflow models. In particular, we address the hybrid nature of CPS models by using different methods to infer properties from continuous and discrete state variables of the model. We evaluate CPSDebug on two case studies, involving two main scenarios and several classes of faults, demonstrating the potential value of our approach.

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Section: Related Workmentioning

confidence: 99%

CPSDebug: Automatic failure explanation in CPS models

Bartocci

Manjunath

Mariani

et al. 2021

Int J Softw Tools Technol Transfer

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“…CPSDebug originally addresses failure explanation in the context of CPS models. The closest work to CPSDebug is probably Hynger [12,21], which exploits invariant generation to detect specification mismatches, that is, a mismatch between an actual and an inferred specification, in Simulink models. Specification mismatches can indicate the presence of problems in the models.…”

Section: Related Workmentioning

confidence: 99%

Automatic Failure Explanation in CPS Models

Bartocci

Manjunath

Mariani

et al. 2019

Software Engineering and Formal Methods

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Debugging Cyber-Physical System (CPS) models can be extremely complex. Indeed, only the detection of a failure is insufficient to know how to correct a faulty model. Faults can propagate in time and in space producing observable misbehaviours in locations completely different from the location of the fault. Understanding the reason of an observed failure is typically a challenging and laborious task left to the experience and domain knowledge of the designer. In this paper, we propose CPSDebug, a novel approach that by combining testing, specification mining, and failure analysis, can automatically explain failures in Simulink/Stateflow models. We evaluate CPSDebug on two case studies, involving two use scenarios and several classes of faults, demonstrating the potential value of our approach.

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“…Daikon analysis tool is a computer program that detects likely invariants of programs [16]. An invariant is a condition that always holds true at certain points in the program.…”

Section: Software Behavior Modeling Toolsmentioning

confidence: 99%

The Improvement of GK-tail Algorithm of Software Behavior Modeling

Zhang¹,

Zhao²,

Zhou³

et al. 2018

dtcse

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Software behavior modeling is very important to analyze software system security. The goal of software behavior analysis is to obtain the semantic description and understanding of the software behavior. Software behavior modeling and testing are the key steps to achieve the goal. Therefore, this paper studies the software behavior model algorithm, GK-tail algorithm. GK-tail algorithm uses interaction trace to describe the software behavior, which is concerned interaction between the software components. However, GK-tail algorithm is not accurate. Because GK-tail algorithm uses Daikon analysis tool to obtain the predicates, which contain some errors. In order to improve the accuracy of the GK-tail algorithm in software behavior modeling, this paper improves the traditional GK-tail algorithm based on the extended finite automaton. The improved GK-tail algorithm utilizes the combination of Daikon and static (ESC/Java) tools to filter out some errors of the generated predicates. In addition, this paper designs and implements a prototype system of the improved GK-tail algorithm. The prototype system proves that the improved GK-tail algorithm can filter out errors in the second step of GK-tail algorithm effectively.

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Cyber-physical specification mismatch identification with dynamic analysis

Cited by 12 publications

References 43 publications

CPSDebug: Automatic failure explanation in CPS models

CPSDebug: Automatic failure explanation in CPS models

Automatic Failure Explanation in CPS Models

The Improvement of GK-tail Algorithm of Software Behavior Modeling

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