Forecasting the efficiency of agile projects using machine learning

Abstract

The aim of the research is to address current challenges in the development of informa-tion systems and the integration of artificial intelligence technologies to optimize Agile meth-odologies. The study focuses on modeling and forecasting the efficiency of Agile processes, taking into account changing requirements over time and random deviations. Forecasting is understood as the process of estimating the future efficiency of Agile processes based on the analysis of time series and machine learning models. Efficiency forecasting is considered as time series forecasting, where parameters adapt depending on the system and data, allowing the model to generalize and take into account various factors affecting project outcomes. The study examines the selection of the optimal model for forecasting Agile process effi-ciency by comparing machine learning methods such as Support Vector Machines (SVM), Random Forest, and neural networks (CNN, LSTM). Comparative analysis showed that neu-ral networks (LSTM, Bidirectional LSTM, CNN) are more effective for predicting project suc-cess, demonstrating high accuracy and lower errors. The practical value of the research lies in identifying LSTM and other neural network architectures as effective tools for predicting project success. This can serve as a guideline for implementing effective management systems in real-world conditions. For example, the application of LSTM in large IT companies for forecasting the success of Agile projects has significantly improved planning accuracy and reduced risks. The research confirms that the integration of machine learning models, particularly LSTM and CNN, significantly improves the accuracy of forecasting and managing Agile pro-jects. The application of these technologies can greatly enhance project management effi-ciency in information systems.