Case based reasoning approach for adaptive test suite optimization

摘要

Case-based reasoning is an approach to problem solving and learning that has got a lot of attention over the last few years. This paper provides an overview of the foundational issues related to case-based reasoning, describing some of the leading methodological approaches within the field, and exemplifying the current state through pointers to some systems. The framework influences the recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval reuse, solution testing, and learning are summarized, and realization is discussed with few example systems that represent different CBR approaches. Regression testing occurs during the maintenance stage of the software life cycle, however, it requires large amounts of test cases to assure the attainment of a certain degree of quality. So, test suite sizes may grow significantly. This paper focuses primarily on application of CBR to test suite optimization.