G. Gousios

G. Gousios is an Assistant Professor in the Department of Software Technology at the Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology. He previously held an assistant professor position at Radboud Universiteit Nijmegen from January 2015 to July 2016. His research focuses on empirical software engineering, software analytics, and big data applications in software development.

His research interests include:

• Empirical analysis of software development practices
• Software analytics and data-driven development
• Agile methodologies and sprint planning automation
• Code review and pull request dynamics
• Dependency and build system analysis
• Predictive modeling for project delays

The recent publications of G. Gousios reflect a strong trend in applying data science and machine learning techniques to real-world software engineering problems. His work spans automated sprint planning, call graph generation, delay prediction using Bayesian models, and empirical studies on pull request decisions. These contributions highlight a focus on improving software development efficiency, reliability, and maintainability through empirical and analytical methods. His research bridges the gap between theoretical models and practical tooling in software engineering.

Scientific awards received:

• ASE 2024 ACM SIGSOFT Distinguished Paper Award

He has supervised four academic works, indicating active involvement in student mentoring and research guidance. Although specific grant details are not mentioned, his consistent publication record in top venues and dataset creation suggest successful engagement in research funding and collaborative projects. He collaborates extensively with prominent researchers such as Arie van Deursen and Emad Shihab.

G. Gousios contributes to the software engineering research community through dataset sharing, such as the 'Catcher' dataset for API misuse detection, promoting reproducibility and open science. His work is integrated into both academic and industrial software development contexts, emphasizing practical impact.