The development of requirements specifications inevitably involves
modification and evolution. To support modification
while preserving the main requirements goals and properties,
we propose the use of a cycle composed of two phases: analysis
and revision. In the analysis phase, a desirable property of the
system is checked against a partial specification. Should the
property be violated, diagnostic information is provided. In
the revision phase, the diagnostic information is used to help
modify the specification in such a way that the new specification
no longer violates the original property.
We have investigated a particular instance of such a cycle
that combines the techniques of logical abduction and inductive
learning to analyse and revise specifications respectively.
Given an (event-based) system description and a system
property, our abductive reasoning mechanism identifies a
set of counter-examples of the property, if any exists. This
set is then used to generate a corresponding set of examples
of system behaviours that should be covered by the system
description. These examples are used as training examples by
our inductive learning mechanism, which performs the necessary
changes to the system description in order to resolve the
property violation. The approach is supported by an abductive
decision procedure and a hybrid (neural and symbolic)
learning system that we have developed. A case study of an
automobile cruise control system illustrates our approach and
provides some early validation of its capabilities.