Adaptive model learning for continual verification of non-functional properties

TY - GEN

T1 - Adaptive model learning for continual verification of non-functional properties

AU - Calinescu, Radu

AU - Rafiq, Yasmin

AU - Johnson, Kenneth

AU - Bakir, Mehmet Emin

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - Discrete-time Markov models

KW - On-line model learning

KW - Runtime quantitative verification

KW - Service-based systems

UR - http://www.scopus.com/inward/record.url?scp=84899669927&partnerID=8YFLogxK

U2 - 10.1145/2568088.2568094

DO - 10.1145/2568088.2568094

M3 - Conference contribution

AN - SCOPUS:84899669927

SN - 978-1-4503-2733-6

SP - 87

EP - 98

BT - ICPE 2014

PB - ACM

T2 - 5th ACM/SPEC International Conference on Performance Engineering, ICPE 2014

Y2 - 22 March 2014 through 26 March 2014

ER -