An Assertion language for slicing Constraint Logic Languages

TL;DR

This paper introduces an assertion language for Constraint Logic Programming (CLP) and extends a dynamic slicing framework from Concurrent Constraint Programming (CCP), enabling automated debugging and analysis of complex logic programs.

Contribution

It generalizes previous CCP slicing methods to CLP and develops an assertion language to automatically identify and isolate incorrect computations in logic programs.

Findings

Framework successfully integrates with existing semi-automatic slicing tools.

Experiments demonstrate improved automation in debugging CLP programs.

The assertion language effectively detects property violations in program states.

Abstract

Constraint Logic Programming (CLP) is a language scheme for combining two declarative paradigms: constraint solving and logic programming. Concurrent Constraint Programming (CCP) is a declarative model for concurrency where agents interact by telling and asking constraints in a shared store. In a previous paper, we developed a framework for dynamic slicing of CCP where the user first identifies that a (partial) computation is wrong. Then, she marks (selects) some parts of the final state corresponding to the data (constraints) and processes that she wants to study more deeply. An automatic process of slicing begins, and the partial computation is "depurated" by removing irrelevant information. In this paper we give two major contributions. First, we extend the framework to CLP, thus generalizing the previous work. Second, we provide an assertion language suitable for both, CCP and CLP, which allows the user to specify some properties of the computations in her program. If a state in a computation does not satisfy an assertion then some "wrong" information is identified and an automatic slicing process can start. This way we make one step further towards automatizing the slicing process. We show that our framework can be integrated with the previous semi-automatic one, giving the user more choices and flexibility. We show by means of examples and experiments the usefulness of our approach.