Towards Parameterized Regular Type Inference Using Set Constraints

Details Towards Parameterized Regular Type Inference Using Set Constraints Towards Parameterized Regular Type Inference Using Set Constraints

2010-02-09

arxiv.org/abs/1002.1836v1

WLPE 2009 Proceedings

Computer Science

Logic in Computer Science

Scientific paper

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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