Moritz Mock

PhD Student

About Me

Moritz Mock is a PhD student in Advanced-Systems Engineering (ASE) at the Free University of Bozen-Bolzano. He holds a Master of Science in Software Engineering for Information Systems from the Free University of Bozen-Bolzano. His main research interest includes mining open-source projects to detect vulnerabilities utilizing static and machine-learning approaches. He is currently working with Prof. Barbara Russo's research group.

Publications

Barbara Russo, Jorge Melegati and Moritz Mock, “Leveraging Multi-Task Learning to Improve the Detection of SATD and Vulnerability,” 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC), Ottawa, ON, Canada, 2025, pp. 01-12.

doi.org/10.1109/ICPC66645.2025.00017

Moritz Mock, Thomas Borsani, Giuseppe Di Fatta and Barbara Russo, “Optimizing Deep Learning Models to Address Class Imbalance in Code Comment Classification,” 2025 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE), Ottawa, ON, Canada, 2025, pp. 45-48.

doi.org/10.1109/NLBSE66842.2025.00016

Moritz Mock, Jorge Melegati, Max Kretschmann, Nicolás E. Díaz Ferreyra, and Barbara Russo. MADE-WIC: Multiple annotated datasets for exploring weaknesses in code. In 39th IEEE/ACM International Conference on Automated Software Engineering (ASE '24), October 28–November 1, 2024, Sacramento, CA, USA. ACM, New York, NY, USA, 4 pages.

doi.org/10.1145/3691620.3695348

Moritz Mock, Thomas Forrer, and Barbara Russo. Where do developers admit their security-related concerns? In: Marchesi, L., et al. Agile Processes in Software Engineering and Extreme Programming – Workshops. XP 2024. Lecture Notes in Business Information Processing, vol 524. Springer, Cham.

doi.org/10.1007/978-3-031-72781-8_3

Moritz Mock, Jorge Melegati, and Barbara Russo. Generative AI for Test Driven Development: Preliminary Results. In: Marchesi, L., et al. Agile Processes in Software Engineering and Extreme Programming – Workshops. XP 2024. Lecture Notes in Business Information Processing, vol 524. Springer, Cham.

doi.org/10.1007/978-3-031-72781-8_21

Moritz Mock. Utilization of Machine Learning for the Detection of Self-admitted Vulnerabilities. In: Kadgien, R., Jedlitschka, A., Janes, A., Lenarduzzi, V., Li, X. (eds) Product-Focused Software Process Improvement. PROFES 2023. Lecture Notes in Computer Science, vol 14484. Springer, Cham.

doi.org/10.1007/978-3-031-49269-3_15

Barbara Russo, Matteo Camilli, and Moritz Mock. WeakSATD: Detecting Weak Self-admitted Technical Debt. In 19th International Conference on Mining Software Repositories (MSR ’22), May 23–24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 6 pages.

dl.acm.org/doi/abs/10.1145/3524842.3528469