Enhancing Software Quality Assurance

Abstract

This thesis investigates how Software Quality Assurance (SQA) can be enhanced by incorporating Artificial Intelligence (AI) into established methods to handle the ever-increasing complexity of today's software systems. SQA is an integral part of software development, ensuring that applications are reliable, functional, and meet the expectations of users. Despite the progress made, traditional SQA methods face challenges such as limited automation, high costs, and inconsistent standards.

This research follows a mixed-method strategy that combines an in-depth literature review with surveys and interviews conducted among experts in the software industry. It studies the capabilities of AI powered tools to perform tasks such as predictive analytics, automated testing, and bug detection, while it also points out challenges such as dependence on data and complications in system designs. A comparative review of quality assurance strategies adopted by both major technology firms and smaller businesses reveals some striking differences in the way automation is applied, and resources are allocated.

The outcome of this study is the proposal for a hybrid Software Quality Assurance framework that effectively merges traditional methods with AI-based enhancements. The proposed framework addresses the problems that have always existed in the field of software testing by improving accuracy, scalability, and efficiency. The results highlight AI's transformative potential in raising the bar for SQA while offering flexibility toward several organizational needs. This thesis serves as a guideline on how to adopt hybrid methods of quality assurance, team collaboration, and meeting the demands of a modern technology-driven landscape.