Knowledge Engineering Support for Intelligent Software Test Optimization

Abstract

Software testing is one of the most important processes in Software Development Life Cycle (SDLC) to ensure quality of software product. A large number of approaches for test data generation and optimization in software testing have been reported with varying degrees of success. In recent years, the application of artificial intelligence (AI) techniques in software testing to achieve quality software is an emerging area of research. It combines the concepts of the two domains AI and Software Engineering and yields innovative mechanisms that have the advantages of both of them. This chapter demonstrates the development of a novel software test sequence optimization framework using intelligent agents based graph searching technique. To ensure the quality, a set of test adequacy criteria such as path coverage, state coverage, and branch coverage have been applied in the approach discussed in this chapter, while achieving optimization.