Towards Parameterized Regular Type Inference Using Set Constraints

TL;DR

This paper introduces a new method for inferring parameterized regular types in logic programs using set constraints, enhancing precision and efficiency over previous approaches.

Contribution

It extends regular type inference with parameterized types that relate different predicates, improving the precision of program analysis.

Findings

The method infers tighter success type approximations.

Enhanced constraint solving improves analysis precision.

Experimental results demonstrate feasibility and efficiency.

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.