Declarative Diagnosis of Missing Answers in Constraint Functional-Logic Programming

We present a declarative method for diagnosing

missing computed answers

in

$CFLP{\mathcal (D)}$

, a generic scheme for lazy

Constraint Functional-Logic Programming

which can be instantiated by any constraint domain

${\mathcal D}$

given as parameter. As far as we know, declarative diagnosis of missing answers in such an expressive framework has not been tackled before. Our approach combines and extends previous work done separately for constraint logic programming and lazy functional programming languages. Diagnosis can be started whenever a user finds that the set of computed answers for a given goal with finite search space misses some expected solution w.r.t. an

intended interpretation

of the program, that provides a declarative description of its expected behavior. Diagnosis proceeds by exploring a

proof tree

, that provides a declarative view of the

answer-collection

process performed by the computation, and it ends up with the detection of some function definition in the program that is incomplete w.r.t. the intended interpretation. We can prove the

logical correctness

of the diagnosis method under the assumption that the recollection of computed answers performed by the goal solving system can be represented as a proof tree. We argue the plausibility of this assumption, and we describe the prototype of a tool which implements the diagnosis method.