Model-checking continuous-time Markov chains
ACM Transactions on Computational Logic (TOCL)
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
PEPA nets: a structured performance modelling formalism
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Self-Managed Systems: an Architectural Challenge
FOSE '07 2007 Future of Software Engineering
Probabilistic Model-Checking Support for FMEA
QEST '07 Proceedings of the Fourth International Conference on Quantitative Evaluation of Systems
Specification patterns for probabilistic quality properties
Proceedings of the 30th international conference on Software engineering
Using quantitative analysis to implement autonomic IT systems
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Model evolution by run-time parameter adaptation
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Monitoring probabilistic properties
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Engineering of Framework-Specific Modeling Languages
IEEE Transactions on Software Engineering
Probabilistic timed behavior trees
IFM'07 Proceedings of the 6th international conference on Integrated formal methods
CBSE'07 Proceedings of the 10th international conference on Component-based software engineering
Hi-index | 0.00 |
Nowadays requirements related to quality attributes such as performance, reliability, safety and security are often considered the most important requirements for software development projects. To reason about these quality attributes different stochastic models can be used. These models enable probabilistic verification as well as quantitative prediction at design time. On the other hand, these models could be also used to perform runtime adaptation in order to achieve certain quality goals. This workshop aims to provide a forum for researchers in these areas that should help with the adoption of quantitative stochastic models into general software development processes.