Analysis of software development quality in Colombia through machine learning models

Abstract

This analysis examines the quality of software development in Colombia through the review and evaluation of the contributions of machine learning models. A qualitative investigation was conducted using a case study design, based on the analysis of scientific articles, technical reports, national policies, and documented experiences from participants in academic and technological environments. Through this method, it was possible to explore perceptions, practices, and evidence related to the application of these models to software, as well as the circumstances under which they are incorporated into development processes. The results indicate that, although artificial intelligence and machine learning have significant potential to predict failures, improve the quality of the final product, and increase productivity, their use is not yet flawless. Additionally, it is evident that developers and companies identify advantages such as early error detection, task automation, and support in decision-making. However, they also face obstacles related to lack of technological maturity, limited local research, and insufficient practical implementation. Overall, the findings indicate the need to strengthen applied research and promote a broader transition toward the use of machine learning models in the field of software development.