Andrea Arcuri

Andrea Arcuri is a Professor of Software Engineering at Kristiania University College in Oslo, Norway, where he leads the AISE lab. Since 2020, he also holds a part-time position as Adjunct Professor at Oslo Metropolitan University. After 5 years in industry (WesternGeco and Scienta/Telenor), he returned to academia full-time in October 2016. His research primarily focuses on automated software testing and search-based software engineering.

Prof. Arcuri's research interests center around Software Testing, Search-Based Software Engineering, and Automated Testing techniques. He has made significant contributions to the field through the development of testing tools like EvoMaster for system-level test generation and EvoSuite for unit test generation. His work bridges theoretical foundations with practical applications, particularly in RESTful API testing and enterprise systems.

His recent publications show a strong trend toward practical applications of search-based testing in industry settings, with particular emphasis on RESTful API testing, fuzz testing, and integration with modern development practices like DevOps. The research spans both theoretical foundations of search-based testing and practical tool development.

• ACM SIGSOFT Impact Paper Award 2023 for EvoSuite
• ICST 2022 10-Year Most Influential Paper Award
• ICSE 2021 10-Year Most Influential Paper Award
• Ranked 2nd Most Active Early Stage SE Researcher in JSS'18
• Multiple Best/Distinguished Paper awards at SSBSE, ASE, and ISSTA

Prof. Arcuri actively supervises PhD students and post-docs, including Iva Kertusha, Susruthan Seran, and Onur Duman. He serves in leadership roles for major conferences, including as FSE 2025 Artifact Track Chair and ICST 2022 Test Tool and Demo Track Chair. His research is supported through various academic grants and industry collaborations.

He leads the AISE (Artificial Intelligence and Software Engineering) lab at Kristiania University College, where his team develops and maintains the EvoMaster system test generation tool. The lab focuses on practical applications of search-based software testing in real-world enterprise settings.