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arXiv · 1002.1836

Towards Parameterized Regular Type Inference Using Set Constraints

F. Bueno·J. Navas·M. Hermenegildo
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Abstract

We propose a method for inferring \emph{parameterized regular types} for logic programs as solutions for systems of constraints over sets of finite ground Herbrand terms (set constraint systems). Such parameterized regular types generalize \emph{parametric} regular types by extending the scope of the parameters in the type definitions so that such parameters can relate the types of different predicates. We propose a number of enhancements to the procedure for solving the constraint systems that improve the precision of the type descriptions inferred. The resulting algorithm, together with a procedure to establish a set constraint system from a logic program, yields a program analysis that infers tighter safe approximations of the success types of the program than previous comparable work, offering a new and useful efficiency vs. precision trade-off. This is supported by experimental results, which show the feasibility of our analysis.

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F. Bueno, J. Navas, M. Hermenegildo. 2010-02-09. Towards Parameterized Regular Type Inference Using Set Constraints. https://arxiv.org/abs/1002.1836

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