EGS: A Transformational Approach to Automatic Example Generation

Abstract

This paper describes a constraint transformation approach to automatic example generation and its implementation. In this approach examples are generated as the result of successive transformations of the constraint formulas. Such transformations are carried out based on various forms of knowledge. Systematic global simplification, largely based on declarative knowledge, mitigates the impact of applying problem-specific and efficient procedural knowledge, with a uniform problem representation scheme. The approach suggests a general framework for example generation in which a language for describing examples can be defined. It also combines a general formal reasoning capacity and problem-specific procedural knowledge, to achieve both generality and efficiency. The implemented system has proven to be expressively powerful and efficient for a variety of applications.