Representing software usage models with stochastic automata networks
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Requirements Definition for Survivable Network Systems
ICRE '98 Proceedings of the 3rd International Conference on Requirements Engineering: Putting Requirements Engineering to Practice
Reliability prediction for component-based software architectures
Journal of Systems and Software - Special issue on: Software architecture - Engineering quality attributes
Usage-based statistical testing of web applications
ICWE '06 Proceedings of the 6th international conference on Web engineering
Towards pro-active adaptation with confidence: augmenting service monitoring with online testing
Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Testing techniques in software engineering
Testing techniques in software engineering
An approach to defining requirements for exceptions
Advanced Topics in Exception Handling Techniques
Testing techniques selection based on ODC fault types and software metrics
Journal of Systems and Software
Hi-index | 0.00 |
When a population is too large for study, as is the case for all possible uses of a software system, a statistically correct sample must be drawn as a basis for inferences about the population. In statistical testing of software based on a Markov chain usage model, the rich body of analytical results available for Markov chains provides numerous insights that can be used in test planning. Further, the connection between Markov chains and operations research techniques permits a Markov usage model to be expressed as a system of constraints, with mathematical programming used to generate the optimal model for a particular objective function. Since a software usage model is based on the specification, all analyses may be performed early in the development cycle and used as a quantitative basis for management decisions. These techniques have been reduced to engineering practice and used in large projects by IBM, Ericsson, all branches of the US military, and others. In this paper, statistical experiments, Markov models, and optimization techniques are shown to provide a sound theoretical and practical basis far quantifying the reliability of software.