Impact Analysis of Intelligent Agents in Automatic Fault-Prone Components Prediction and Testing

Abstract

Software quality is imperative for industrial strength software. This quality will be often determined by a few components present in the software which decides the entire functionality. If any of these components are not rigorously tested, the quality will be highly affected. Without knowing which of these components are really critical, it will not be possible to perform high level testing. Hence, to predict such fault-prone or critical components from the software prior to testing and prioritizing them during the testing process, an agent-based approach is proposed in this chapter. The framework developed as part of this work will certainly reduce the field failures and thus will improve the software quality. Further, this approach has also utilized important metrics to predict such components and also prioritized the components based on their critical value. Also, the work proposed in this research has also been compared with some of the existing approaches and the results reveal that, this work is a novel one and can both predict and test the components from the software.