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Properties to be verified are expressed as Linear Temporal Logic (LTL) formulas, which are negated and then converted into Büchi automata as part of the model-checking algorithm. In addition to model-checking, SPIN can also operate as a simulator, following one possible execution path through the system and presenting the resulting execution ...
In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).
MCL: Model Checking Language; Alternation-Free Modal μ-calculus extended with user-friendly regular expressions and value-passing constructs; subsumes CTL and LTL. mCRL2 mu-calculus: Kozen's propositional modal μ-calculus (excluding atomic propositions), extended with: data-depended processes, quantification over data types, multi-actions ...
These can be arranged into a 2×2 contingency table (confusion matrix), conventionally with the test result on the vertical axis and the actual condition on the horizontal axis. These numbers can then be totaled, yielding both a grand total and marginal totals. Totaling the entire table, the number of true positives, false negatives, true ...
PRISM is a probabilistic model checker, a formal verification software tool for the modelling and analysis of systems that exhibit probabilistic behaviour. [1] PRISM was introduced around 2002 in the context of Parker's PhD work and is still under active development (as of 2024).
Even though the accuracy is 10 + 999000 / 1000000 ≈ 99.9%, 990 out of the 1000 positive predictions are incorrect. The precision of 10 / 10 + 990 = 1% reveals its poor performance. As the classes are so unbalanced, a better metric is the F1 score = 2 × 0.01 × 1 / 0.01 + 1 ≈ 2% (the recall being 10 + 0 / 10 ...
In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).
The task addressed by BLAST is the need to check whether software satisfies the behavioral requirements of its associated interfaces. BLAST employs counterexample -driven automatic abstraction refinement to construct an abstract model that is then model-checked for safety properties.