Formal Verification With Confidence Intervals to Establish Quality of Service Properties of Software Systems


Formal verification is employed to determine the compliance of software and hardware systems with necessary categories of needs. System compliance with useful necessities is usually analyzed using techniques like model checking, and theorem proving. Similarly, a way known as quantitative verification supports the analysis of the reliability, performance, and other quality-of-service (QoS) properties of systems that exhibit stochastic behavior. During this paper, we have a tendency to extend the applicability of quantitative verification to the common situation when the probabilities of transition between some or all states of the Markov models analyzed by the technique are unknown, however observations of those transitions are accessible. To the current finish, we have a tendency to introduce a theoretical framework, and a tool chain that establish confidence intervals for the QoS properties of a software system modelled as a Markov chain with unsure transition possibilities. We tend to use 2 case studies from different application domains to assess the effectiveness of the new quantitative verification technique. Our experiments show that disregarding the on top of source of uncertainty might significantly affect the accuracy of the verification results, resulting in wrong decisions, and low-quality software systems.

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