Next: Abstract Interpretation Up: Probabilistic Abstract Interpretation Previous: Probabilistic Abstract Interpretation

Background

Theoretical and practical experience show that the verification and analysis of computer programs is hard and sometimes unsolvable. It is therefore reasonable to pursue approaches which simplify this task even if they contain some degrees of imprecision, by which we do not mean linguistic vagueness (``Mike is handsome'' or ``John has an IQ=94''), but rather the lack of some specific information (``$x$ is in the range $[0,4]$'' or ``$x$ has a normal distribution''). Methods involving imprecision are able to produce only partial or approximate results, where approximate means here ``precise at a coarser level of detail''. One well-known framework for such a kind of analysis is Abstract Interpretation [16, 40].

 
• Abstract Interpretation
• Open Problem: Error Quantification
• Probabilistic Semantics

Next: Abstract Interpretation Up: Probabilistic Abstract Interpretation Previous: Probabilistic Abstract Interpretation