Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Database Systems Concepts
Software Abstractions: Logic, Language, and Analysis
Software Abstractions: Logic, Language, and Analysis
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
MiniZinc: towards a standard CP modelling language
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Satisfiability modulo theories: introduction and applications
Communications of the ACM
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Partial models: a position paper
Proceedings of the 8th International Workshop on Model-Driven Engineering, Verification and Validation
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Towards a Methodology for Verifying Partial Model Refinements
ICST '12 Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation
Language independent refinement using partial modeling
FASE'12 Proceedings of the 15th international conference on Fundamental Approaches to Software Engineering
Partial models: towards modeling and reasoning with uncertainty
Proceedings of the 34th International Conference on Software Engineering
Managing requirements uncertainty with partial models
RE '12 Proceedings of the 2012 IEEE 20th International Requirements Engineering Conference (RE)
Change propagation due to uncertainty change
FASE'13 Proceedings of the 16th international conference on Fundamental Approaches to Software Engineering
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Uncertainty is pervasive in Model-based Software Engineering. In previous work, we have proposed partial models as a way to explicate uncertainty during modeling. Using partial models, modelers can perform certain forms of reasoning, like checking properties, without the having to prematurely resolve uncertainty. In this paper, we present a strategy for encoding partial models into different reasoning formalisms and conduct an empirical study aimed to compare the effectiveness of these formalisms for checking properties of partial models.