Abstract
Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification depends on how accurately the parameters of these models can be
estimated using data from component unit testing, monitoring, system logs, etc. When insufficient data are available, the models are affected by epistemic parametric uncertainty, the verification results are inaccurate, and any engineering decisions based on them may be invalid. To address these problems, we introduce VERACITY, a tool-supported iterative approach for the efficient and accurate verification of nonfunctional requirements under epistemic parameter uncertainty. VERACITY integrates confidence-interval quantitative verification with a new adaptive uncertainty reduction heuristic that collects additional data about the parameters of the verified model by unit-testing specific system components over a series of verification iterations. VERACITY supports the quantitative verification of discrete-time Markov chains, deciding which components are to be tested in each iteration based on factors that include the sensitivity of the model to variations in the parameters of different components, and the overheads (e.g., time or cost) of unit-testing each of these
components. We show the effectiveness and efficiency of VERACITY by using it for the verification of the nonfunctional requirements of a tele-assistance service-based system and an online shopping web application.
estimated using data from component unit testing, monitoring, system logs, etc. When insufficient data are available, the models are affected by epistemic parametric uncertainty, the verification results are inaccurate, and any engineering decisions based on them may be invalid. To address these problems, we introduce VERACITY, a tool-supported iterative approach for the efficient and accurate verification of nonfunctional requirements under epistemic parameter uncertainty. VERACITY integrates confidence-interval quantitative verification with a new adaptive uncertainty reduction heuristic that collects additional data about the parameters of the verified model by unit-testing specific system components over a series of verification iterations. VERACITY supports the quantitative verification of discrete-time Markov chains, deciding which components are to be tested in each iteration based on factors that include the sensitivity of the model to variations in the parameters of different components, and the overheads (e.g., time or cost) of unit-testing each of these
components. We show the effectiveness and efficiency of VERACITY by using it for the verification of the nonfunctional requirements of a tele-assistance service-based system and an online shopping web application.
| Original language | English |
|---|---|
| Article number | 111275 |
| Number of pages | 19 |
| Journal | Journal of Systems and Software |
| Volume | 188 |
| Early online date | 22 Feb 2022 |
| DOIs | |
| Publication status | E-pub ahead of print - 22 Feb 2022 |
Bibliographical note
© 2022 Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.Keywords
- quantitative verification
- probabilistic model checking
- confidence intervals
- uncertainty reduction
- nonfunctional requirements
- unit testing