5 research outputs found
On derived dependencies and connected databases
AbstractThis paper introduces a new class of deductive databases (connected databases) for which SLDNF-resolution never flounders and always computes ground answers. The class of connected databases properly includes that of allowed databases. Moreover the definition of connected databases enables evaluable predicates to be included in a uniform way. An algorithm is described which, for each predicate defined in a normal database, derives a propositional formula (groundness formula) describing dependencies between the arguments of that predicate. Groundness formulae are used to determine whether a database is connected. They are also used to identify goals for which SLDNF-resolution will never flounder and will always compute ground answers on a connected database
Detecting Prolog programming techniques using abstract interpretation
There have been a number of attempts at developing intelligent tutoring systems (ITSs)
for teaching students various programming languages. An important component of such
an ITS is a debugger capable of recognizing errors in the code the student writes and
possibly suggesting ways of correcting such errors. The debugging process involves a
wealth of knowledge about the programming language, the student and the individual
problem at hand, and an automated debugging component makes use of a number of
tools which apply this knowledge. Successive ITSs have incorporated a wider range of
knowledge and more powerful tools.
The research described in this thesis should be seen as carrying on with this succes¬
sion. Specifically, we attempt to enhance an existing Prolog ITS (PITS) debugger called
APR0P0S2 developed by Looi. The enhancements take the form of a richer language
with which to describe Prolog code and more powerful tools with which constructs in
this language may be detected in Prolog code.
The richer language is based on the notion of programming techniques—common
patterns in code which capture in some sense an expert's understanding of Prolog.
The tools are based on Prolog abstract interpretation—a program analysis method for
inferring dynamic properties of code. Our research makes contributions to both these
areas.
We develop a language for describing classes of Prolog programming techniques
that manipulate data-structures. We define classes in this language for common Prolog
techniques such as accumulator pairs and difference structures.
We use abstract interpretation to infer the dynamic features with which techniques
are described. We develop a general framework for abstract interpretation which is
described in Prolog, so leading directly to an implementation. We develop two abstract
domains—one which infers general data flow information about the code and one which
infers particularly detailed type information—and describe the implementation of the
former