Adaptive model learning for continual verification of non-functional properties

Radu Calinescu, Yasmin Rafiq, Kenneth Johnson, Mehmet Emin Bakir

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A growing number of business and safety-critical services are delivered by computer systems designed to reconfigure in response to changes in workloads, requirements and internal state. In recent work, we showed how a formal technique called continual verification can be used to ensure that such systems continue to satisfy their reliability and performance requirements as they evolve, and we presented the challenges associated with the new technique. In this paper, we address important instances of two of these challenges, namely the maintenance of up-to-date reliability models and the adoption of continual verification in engineering practice. To address the first challenge, we introduce a new method for learning the parameters of the reliability models from observations of the system behaviour. This method is capable of adapting to variations in the frequency of the available system observations, yielding faster and more accurate learning than existing solutions. To tackle the second challenge, we present a new software engineering tool that enables developers to use our adaptive learning and continual verification in the area of service-based systems, without a formal verification background and with minimal effort. Copyright is held by the owner/author(s). Publication rights licensed to ACM.

Original languageEnglish
Title of host publicationICPE 2014
Subtitle of host publicationProceedings of the 5th ACM/SPEC International Conference on Performance Engineering
Number of pages12
ISBN (Print)978-1-4503-2733-6
Publication statusPublished - 1 Jan 2014
Event5th ACM/SPEC International Conference on Performance Engineering, ICPE 2014 - Dublin, United Kingdom
Duration: 22 Mar 201426 Mar 2014


Conference5th ACM/SPEC International Conference on Performance Engineering, ICPE 2014
Country/TerritoryUnited Kingdom


  • Discrete-time Markov models
  • On-line model learning
  • Runtime quantitative verification
  • Service-based systems

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