Global Software Development (GSD) brings together teams from diverse regions and cultural backgrounds, allowing for the pooling of varied expertise and perspectives. However, this international collaboration often comes with significant challenges, such as communication barriers, trust issues, and differing work practices. These challenges can hinder the smooth functioning of development teams and impact the overall success of software projects. In this study, we explore the role of artificial intelligence (AI) in predicting and addressing the cross-cultural obstacles that arise in GSD environments. The research utilizes several machine learning models to analyze and predict the potential challenges associated with cross-cultural communication and collaboration. These models include Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression (SVR), and XGBoost. After evaluating the performance of these models, we found that Ridge Regression and XGBoost yielded the most accurate predictions in this context. Model effectiveness was assessed using key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results of this study provide valuable insights into the use of AI as a tool for identifying and addressing cultural issues within global software teams. By leveraging AI to predict potential cross-cultural conflicts, development teams can implement proactive strategies to foster better communication, build trust, and align work practices, ultimately enhancing the efficiency and success of global software development projects. These findings demonstrate the potential for AI to serve as a strategic resource in managing and overcoming the challenges inherent in distributed software development environments.  

Keywords - Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression (SVR), XGBoost, Machine Learning Models, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error

[1] Ali, N., & Lai, R. (2021). Global software development: a review of its practices. Malaysian Journal of Computer Science, 34(1), 82-129.

[2] Ting-Toomey, S., & Dorjee, T. (2018). Communicating across cultures. Guilford Publications.

[3] Olson, J. S., & Olson, G. (2013). Working together apart: Collaboration over the internet. Morgan & Claypool Publishers.

[4] Demir, S., & Sahin, E. K. (2023). Application of state-of-the-art machine learning algorithms for slope stability prediction by handling outliers of the dataset. Earth Science Informatics, 16(3), 2497-2509.

[5] Correia, A. B. (2023). The rise of populist attitudes: Using supervised machine learning to identify their main determinants.

[6] Salama, M. (2024). Optimization of Regression Models Using Machine Learning: A Comprehensive Study with Scikit-learn. Optimization of Regression Models Using Machine Learning: A Comprehensive Study with Scikit-learn| IUSRJ, 5.

[7] Chan, J. Y. L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z. W., & Chen, Y. L. (2022). Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics, 10(8), 1283.