Using Deep Learning and Dependency Matrix Structure Perform Optimization and Increase the Accuracy Rate

Abstract

Software Testing is a perceived software development life cycle methodology, which is used to test the software product quality by writing test logic that matches with customer requirements. Interactions/relationships between systems or sub systems or modules in an application are called dependency. We use software in different contexts like Aircrafts, Medical Equipment’s, Stock exchanges, Space systems, banks, Machine production etc., Software also manage enterprises and their bonding to clients and suppliers. It also supports taking strategic decisions in business organizations. Reliability and performance of software is very crucial to consider for effective management of our systems. Earlier in the past, few techniques have been derived considering dependency structures in applications which enables to select test case prioritization both manually and using internal or open-source or commercial vendor based automated tools. This paper analyzes the application dependency structure algorithms to effectively plan the module sequences prioritize the optimized test cases using a novel Deep Diverse Prototype Forest Model  by improving performance and efficiency.