Nowadays, technology plays a fundamental role in data collection and analysis, which are essential for decision-making in various fields. Agile methodologies have transformed project management by focusing on continuous delivery and adaptation to change. In multiple project management, assessing the progress and pace of work in Sprints is particularly important. In this work, a data model was developed to evaluate the progress and pace of work, based on the visual interpretation of numerical data from certain graphs that allow tracking, such as the Burndown chart. Additionally, experiments with machine learning algorithms were carried out to validate the effectiveness and potential improvements facilitated by this dataset development.

Agile frameworks continue to provide positive evidence regarding the benefits of their use in the software products. Since these methods develop professional skills in those who practice them, their knowledge and use will acquire greater demand in areas other than software development. For this reason, it is essential to recognize the key skills for agile team building. The goal of this paper is to identify the agile professional skills that the Chilean industry considers key to conform high-performance agile teams. A survey was applied to agile community professionals in Chile to validate the results of previous work and to identify relevant information regarding learning processes, techniques, and tools for working with agile frameworks. The results allowed to establish three key skills for high-performance teams with their respective levels of achievement.

Since the rise of agile methods, it has become important to maintain their management and monitoring to succeed in the transformation process from a traditional approach to an agile one. In addition to this, several authors have used Machine Learning models to support prediction or estimation processes in the Project Management framework. However, there are current challenges and areas of opportunity in relation to Agile Project Management in combination with Machine Learning. Therefore, in this paper, we have conducted a systematic review of the literature to know the current state of Machine Learning applied to Agile Project Management, in order to know which techniques are currently the most used and thus detect new areas of opportunity.