Probabilistic Verification at Runtime for Self-Adaptive Systems

Filieri, Antonio; Tamburrelli, Giordano

doi:10.1007/978-3-642-36249-1_2

Cited by 28 publications

(15 citation statements)

References 29 publications

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“…Designing and deploying certifiable verification and validation methods for self-adaptive applications is one of the major research challenges for the software engineering community in general and the selfadaptive applications community in particular [11]. Concerned properties for self-adaptive applications include safety [13,40], liveness and reachability [21,28,29,40], reliability [7,17], and stability [4,28,29]. In recent years, different methods have been used in [40,4,5] to verify self-adaptive applications.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to environmental constraints, overlooking such uncertainty can also lead to inaccurate verification results. However, to the best of our knowledge, none of existing work [4,7,17,21,28,29,40] has explicitly modeled and considered such uncertainty in verifying self-adaptive applications.…”

Section: Introductionmentioning

confidence: 95%

“…Given an application, these techniques exhaustively explore its state space to detect potential errors (e.g., dead state or abnormal behavior). Recent studies have also reported promising results in applying these techniques to verifying properties of self-adaptive applications, e.g., safety [13,40], reliability [7,17], liveness and reachability [21,28,29,40], consistency [21] and stability [4,28,29]. However, when it comes to verifying self-adaptive applications under real-world environments, these pieces of existing work have two major limitations:…”

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Verifying self-adaptive applications suffering uncertainty

Yang

Liu

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Self-adaptive applications address environmental dynamics systematically. They can be faulty and exhibit runtime errors when environmental dynamics are not considered adequately. It becomes more severe when uncertainty exists in their sensing and adaptation to environments. Existing work verifies self-adaptive applications, but does not explicitly consider environmental constraints or uncertainty. This gives rise to inaccurate verification results. In this paper, we address this problem by proposing a novel approach to verifying self-adaptive applications suffering uncertainty in their environmental interactions. It builds Interactive State Machine (ISM) models for such applications and verifies them with explicit consideration of environmental constraints and uncertainty. It then refines verification results by prioritizing counterexamples according to their probabilities. We experimentally evaluated our approach with real-life self-adaptive applications, and the experimental results confirmed its effectiveness. Our approach reported 200-660% more counterexamples than not considering uncertainty, and eliminated all false counterexamples caused by ignoring environmental constraints.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 96%

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Verifying self-adaptive applications suffering uncertainty

Yang

Liu

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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“…Since its introduction in Daws' seminal work [20] in 2004, parametric model checking has underpinned the development of a vast array of methods for the modelling and analysis of software and other computer-based systems. These include methods for comparing alternative system designs [30], [31], sensitivity analysis [26], parameter synthesis [8], [9], [22], [32], probabilistic model repair [3], [16], dynamic reconfiguration of self-adaptive systems [14], [21], [24], [25], and synthesis of confidence intervals for the QoS properties of software systems [10], [11]. These methods address very different problems, and yet most researchers who developed them mention the same limitation of parametric model checking: its computationally intensive nature.…”

Section: Related Workmentioning

confidence: 99%

Efficient Parametric Model Checking Using Domain Knowledge

Călinescu

Paterson

Johnson

2021

IIEEE Trans. Software Eng.

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We introduce an efficient parametric model checking (ePMC) method for the analysis of reliability, performance and other quality-of-service (QoS) properties of software systems. ePMC speeds up the analysis of parametric Markov chains modelling the behaviour of software by exploiting domain-specific modelling patterns for the software components. To this end, ePMC precomputes closed-form expressions for key QoS properties of such patterns, and uses these expressions in the analysis of whole-system models.To evaluate ePMC, we show that its application to service-based systems and multi-tier software architectures reduces analysis time by several orders of magnitude compared to current parametric model checking methods.

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“…They note that many of the formal methods used for assuring planning happen during the design of the self-adaptive system, and not at run time. Filieri [24,25], on the other hand, describe a number of strategies for using probabilistic model checking at run time for self-adaptive systems where goals are expressed as temporal logical formulas, including state elimination and algebraic approaches for making it more tractable at run time. Execution.…”

Section: Evidence Types For Use In Assurance Case Decompositionmentioning

confidence: 99%

Challenges in Composing and Decomposing Assurances for Self-Adaptive Systems

Schmerl

Andersson

Vogel

et al. 2017

Software Engineering for Self-Adaptive Systems III. Assurances

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Abstract. Self-adaptive software systems adapt to changes in the environment, in the system itself, in their requirements, or in their business objectives. Typically, these systems attempt to maintain system goals at run time and often provide assurance that they will meet their goals under dynamic and uncertain circumstances. While significant research has focused on ways to engineer selfadaptive capabilities into both new and legacy software systems, less work has been conducted on how to assure that self-adaptation maintains system goals. For traditional, especially safety-critical software systems, assurance techniques decompose assurances into sub-goals and evidence that can be provided by parts of the system. Existing approaches also exist for composing assurances, in terms of composing multiple goals and composing assurances in systems of systems. While some of these techniques may be applied to self-adaptive systems, we argue that several significant challenges remain in applying them to self-adaptive systems in this chapter. We discuss how existing assurance techniques can be applied to composing and decomposing assurances for self-adaptive systems, highlight the challenges in applying them, summarize existing research to address some of these challenges, and identify gaps and opportunities to be addressed by future research.

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Probabilistic Verification at Runtime for Self-Adaptive Systems

Cited by 28 publications

References 29 publications

Verifying self-adaptive applications suffering uncertainty

Verifying self-adaptive applications suffering uncertainty

Efficient Parametric Model Checking Using Domain Knowledge

Challenges in Composing and Decomposing Assurances for Self-Adaptive Systems

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