ARTIFICIAL INTELLIGENCE-DRIVEN SOFTWARE EVALUATION: TECHNIQUES, CHALLENGES, AND FUTURE DEVELOPMENTS

Abstract

This paper examines the impact of artificial intelligence (AI) on software evaluation, highlighting how AI-driven techniques have influenced the assessment and optimization of software systems. It discusses key methodologies, including machine learning algorithms for predictive analytics and automated testing frameworks. Additionally, it addresses challenges such as ensuring high-quality input data, reducing bias in AI models, and bridging skill gaps among developers. Furthermore, it considers future changes that could improve software development, such as generative models and real-time monitoring tools. By covering current uses and future possibilities, this study offers insights into how organizations can use these technologies to increase efficiency while handling ethical concerns.