A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Safeware: system safety and computers
Safeware: system safety and computers
From object-oriented to goal-oriented requirements analysis
Communications of the ACM
Generating statechart designs from scenarios
Proceedings of the 22nd international conference on Software engineering
Bandera: extracting finite-state models from Java source code
Proceedings of the 22nd international conference on Software engineering
An Empirical Investigation of Multiple Viewpoint Reasoning in Requirements Engineering
RE '99 Proceedings of the 4th IEEE International Symposium on Requirements Engineering
On Two Problems in the Generation of Program Test Paths
IEEE Transactions on Software Engineering
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
In fast-paced software projects, engineers don't have the time or the resources to build heavyweight complete descriptions of their software. The best they can do is lightweight incomplete descriptions which may contain missing and contradictory information. Reasoning about incomplete and contradictory knowledge is notoriously difficult. However, recent results from the empirical AI community suggest that randomized search can tame this difficult problem. In this article we demonstrate the the relevance and the predictability of randomized search for reasoning about lightweight models.