An Intelligent Approach to Code-Driven Test Execution

Abstract

In the constantly evolving world of software development, it is crucial to have effective testing methodologies in order to ensure the strength and reliability of applications. This scholarly article presents a new and intelligent approach to test execution that is driven by code and utilizes machine learning to greatly improve adaptability and accuracy in testing processes. Traditional testing methods often struggle to handle changes in code, resulting in less than optimal test execution. Our proposed method utilizes machine learning techniques to predict the impact of code modifications on test results, allowing for a more precise test execution strategy. We have demonstrated significant improvements in test execution efficiency, reducing unnecessary tests and speeding up feedback cycles. The following discussion examines these findings, addresses potential limitations, and suggests future areas for improvement and expansion. Notably, our methodology explains how Git commits aid in updating features, and how the machine learning model predicts the updated feature names. This predicted feature name is then integrated into BehaviorDriven Development (BDD) test selection and execution using standard BDD frameworks. By seamlessly incorporating machine learning into the testing process, developers can achieve greater precision and effectiveness, making significant progress in overcoming challenges posed by changes in code in modern development environments.