Assessing the Impact of Artificial Intelligence and Machine Learning Tools on Software Development Efficiency in Agile Frameworks: A Structured Evaluation Using Machine Learning Models - Ram, Malakit L.; Tagud, Jorton A.; Agoylo Jr, Jose C.; Escabosa, Lesther; Vergara, Christian Jay; Boca, Herbie; Abande, Shaina (Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2025)
Adopting artificial intelligence (AI) and machine learning (ML) in software development processes presents an opportunity to systematically assess improvements in efficiency, accuracy, and project management. However, evaluating these technologies requires structured assessment models rather than generalized claims. This study utilizes a Kaggle dataset and applies linear regression, random forest classifiers, and K-means clustering to examine the impact of AI tools within Agile frameworks. The analysis reveals that AI tools enhance decision-making, productivity, and resource allocation in Agile environments. The linear regression model predicts willingness to adopt AI tools based on key variables, while the random forest classifier achieves high precision and recall in distinguishing AI tool users. Additionally, K-means clustering uncovers distinct adoption patterns among various roles, offering further insights into how AI adoption varies within Agile teams. Rather than assuming AI and ML’s impact, this study systematically evaluates their role in software development efficiency, providing a structured evaluation beneficial to both researchers and practitioners. While the findings highlight AI’s potential for optimizing Agile processes, they are constrained by the dataset’s scope. Future research should incorporate real-world industry validation and broader datasets to further substantiate AI’s effectiveness in Agile frameworks. This research contributes to the ongoing discourse on AI and ML adoption in software development, advocating for data-driven approaches in achieving scalable, efficient, and reliable software development processes.