2024S

• Web Engineering / 194.161 / VU

• Connecting the .dotfiles: Checked-In Secret Exposure with Extra (Lateral Movement) Steps / Jungwirth, G., Saha, A., Schröder, M., Fiebig, T., Lindorfer, M., & Cito, J. (2023). Connecting the .dotfiles: Checked-In Secret Exposure with Extra (Lateral Movement) Steps. In IEEE/ACM 20th International Conference on Mining Software Repositories (MSR) (pp. 322–333). https://doi.org/10.1109/MSR59073.2023.00051
  Project: IoTIO (2020–2025)
• Grammar Inference for Ad Hoc Parsers / Schröder, M. (2022). Grammar Inference for Ad Hoc Parsers. In Companion Proceedings of the 2022 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH Companion ’22) (pp. 38–42). Association for Computing Machinery. https://doi.org/10.1145/3563768.3565550
• Discovering Feature Flag Interdependencies in Microsoft Office / Schröder, M., Kevic, K., Gopstein, D., Murphy, B., & Beckmann, J. (2022). Discovering Feature Flag Interdependencies in Microsoft Office. In ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1419–1429). Association for Computing Machinery. https://doi.org/10.1145/3540250.3558942
  Download: PDF (814 KB)
• Grammars for Free: Toward Grammar Inference for Ad Hoc Parsers / Schröder, M., & Cito, J. (2022). Grammars for Free: Toward Grammar Inference for Ad Hoc Parsers. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: New Ideas and Emerging Results (pp. 41–45). Association for Computing Machinery. https://doi.org/10.1145/3510455.3512787
• An Empirical Investigation of Command-Line Customization / Schröder, M., & Cito, J. (2022). An Empirical Investigation of Command-Line Customization. Empirical Software Engineering, 27(2), Article 30. https://doi.org/10.1007/s10664-021-10036-y
  Download: PDF (1.85 MB)
• Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation / Hofstätter, S., Althammer, S., Schröder, M., Sertkan, M., & Hanbury, A. (2020). Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation (p. 8). arXiv. http://hdl.handle.net/20.500.12708/141680
• Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering / Hofstätter, S., Zlabinger, M., Sertkan, M., Schröder, M., & Hanbury, A. (2020). Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. CIKM 2020: International Conference on Information & Knowledge Management 2020, Virtual Event, Ireland, EU. Association for Computing Machinery. https://doi.org/10.1145/3340531.3412878
• Durability and contention in software transactional memory / Schröder, M. (2015). Durability and contention in software transactional memory [Diploma Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/158792