Towards Parameterized Regular Type Inference Using Set Constraints
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.
Keywords
Cite
@article{arxiv.1002.1836,
title = {Towards Parameterized Regular Type Inference Using Set Constraints},
author = {F. Bueno and J. Navas and M. Hermenegildo},
journal= {arXiv preprint arXiv:1002.1836},
year = {2010}
}