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Theoretical Computer Science
Theoretical Computer Science
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
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Alternating Refinement Relations
CONCUR '98 Proceedings of the 9th International Conference on Concurrency Theory
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Theoretical Computer Science - Logic, semantics and theory of programming
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Theoretical Computer Science
Weighted automata and weighted logics
Theoretical Computer Science
Comparing software metrics tools
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
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IEEE Transactions on Software Engineering
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CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
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LICS '09 Proceedings of the 2009 24th Annual IEEE Symposium on Logic In Computer Science
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IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
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IBM Journal of Research and Development
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ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
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FMOODS'12/FORTE'12 Proceedings of the 14th joint IFIP WG 6.1 international conference and Proceedings of the 32nd IFIP WG 6.1 international conference on Formal Techniques for Distributed Systems
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While a boolean notion of correctness is given by a preorder on systems and properties, a quantitative notion of correctness is defined by a distance function on systems and properties, where the distance between a system and a property provides a measure of "fit" or "desirability." In this article, we explore several ways how the simulation preorder can be generalized to a distance function. This is done by equipping the classical simulation game between a system and a property with quantitative objectives. In particular, for systems that satisfy a property, a quantitative simulation game can measure the "robustness" of the satisfaction, that is, how much the system can deviate from its nominal behavior while still satisfying the property. For systems that violate a property, a quantitative simulation game can measure the "seriousness" of the violation, that is, how much the property has to be modified so that it is satisfied by the system. These distances can be computed in polynomial time, since the computation reduces to the value problem in limit average games with constant weights. Finally, we demonstrate how the robustness distance can be used to measure how many transmission errors are tolerated by error correcting codes.