Software Quality Testing Framework based on Machine Learning Analysis

Abstract

This research work presents a software quality testing framework based on machine learning analysis. The framework utilizes dynamic symbol technology and integration testing methods to analyze different execution paths, thereby establishing a comprehensive integration testing framework. Technologies such as robot framework, exploration-driven test data generation, and software reliability coupling measurement are employed to improve testing efficiency and ensure thorough verification of software functions and performance. The research demonstrates the application of reinforcement learning to test case sequencing, using Q-learning to optimize API functional test case generation. The proposed methodology involves the integration of machine learning analysis into three aspects: information handling, procedure formulation, and execution flow. The paper explores regression testing, test case prioritization technology (TCP), and reinforcement learning for efficient test case ordering. A comprehensive simulation of 500 software reliability testing use cases shows significant improvements in test efficiency by reducing redundant instances. The research concludes with a discussion of the application of Q-Learning in continuous integration testing, emphasizing the need for flexible memory representations to handle complex states and action sets. The proposed framework effectively addresses the challenges posed by scale expansion in software development, thereby improving the accuracy and efficiency of software testing.