The International Conference on Quantitative Evaluation of SysTems (QEST) is the leading forum on quantitative evaluation and verification of computer systems and networks. Areas of interest include quantitative specification methods, stochastic models, and metrics for performance, reliability, safety, correctness, and security. QEST is interested in both theoretical and experimental research. QEST welcomes a diversity of modeling formalisms, programming languages and methodologies that incorporate quantitative aspects such as probabilities, approximations and other quantitative aspects. Papers may advance empirical, simulation and analytic methods. Of particular interest are case studies that highlight the role of quantitative specification, modeling and evaluation in the design of systems. Systems of interest include computer hardware and software architectures, communication systems, cyber-physical systems, infrastructural systems, and biological systems. Papers that describe novel tools to support the practical application of research results in all of the above areas are also welcome.
- Smart Energy Systems over the Cloud
We solicit contributions dealing with quantitative analysis, verification, and performance evaluation of models of networks of smart devices interconnected physically and over the cloud, and in particular within the technological context of smart energy, dealing with smart buildings, the smart grid, or with modern power networks. Instances of problems of interest are energy management in smart buildings, demand response over smart grids, or frequency control over power networks. We are interested in configurations related to cyber-physical systems, of systems of systems, and of the Internet of things, and on models encompassing continuous and digital components, and uncertainty (either environmental, adversarial, or probabilistic).
- Machine Learning and Formal
We call for contributions on the fusion of formal methods and machine learning techniques. In particular, we are interested in the use of machine learning approaches, such as reinforcement learning, learning automata, decision trees, gradient based methods, etc. in (statistical) model checking, controller synthesis, program analysis and synthesis, timed systems, compositional verification, etc. The main aim is to disseminate learning based techniques that have potential of improving theory and practice of formal methods.
|Abstract submission (optional):||
2 April 2017
|Paper and tool submission (strict):||
9 April 2017
(anywhere on earth)
|Author notification:||29 May 2017|
|Final version due:||23 June 2017|
|Conference:||5-7 September 2017|
QEST considers five types of papers:
- Theoretical: advance our understanding, apply to non-trivial problems and be mathematically rigorous.
- Methodological and technical: describe situations that require the development and proposal of new analysis processes and techniques.
- Application: describes a novel application, and compares with previous results.
- Tools: should motivate the development of the new tools and the formalisms they support, with a focus on the software architecture and practical capabilities.
- Tool demonstration: describe a relevant tool, as well as its features, evaluation, or any other information that may demonstrate the merits of the tool.
All accepted papers (including tool demonstrations) must be presented at the conference by one of the authors. The QEST 2017 proceedings will be published by Springer in the LNCS Series and indexed by ISI Web of Science, Scopus, ACM Digital Library, dblp, Google Scholar.