2022W

• Advanced Software Engineering / 180.456 / VO
• Bachelor Thesis / 188.919 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar in Software Engineering / 194.127 / SE

2023S

• Bachelor Thesis / 188.919 / PR
• Informatics Lab (Freifachpraktikum) / 188.416 / PR
• Project in Computer Science / 194.079 / PR
• Project in Computer Science 2 / 194.080 / PR
• Seminar for Master Students in Software Engineering & Internet Computing / 180.777 / SE
• Web Engineering / 188.951 / VU

• Cloud Open Source Research Mobility Network
  2023 – 2026 / European Commission
• EFFECTIVE CONSTRUCTION OF PROBABILISTIC HYBRID SEMI-PARAMETRIC MODELS FOR MODEL-BASED CONDITION MONITORING AND ACCELERATED MATERIAL DESIGN
  2022 – 2027 / Austrian Research Promotion Agency (FFG)
• Software Assistants for Probabilistic Programming
  2022 – 2026 / Meta Platforms, Inc.

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.

• 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
• Learning CI Configuration Correctness for Early Build Feedback / Santolucito, M., Zhang, J., Zhai, E., Cito, J., & Piskac, R. (2022). Learning CI Configuration Correctness for Early Build Feedback. In Proceedings 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (pp. 1006–1017). IEEE. https://doi.org/10.1109/SANER53432.2022.00118
• An Empirical Investigation of Command-Line Customization / Schröder, M., & Cito, J. (2022). An Empirical Investigation of Command-Line Customization. Empirical Software Engineering, 27(30). https://doi.org/10.1007/s10664-021-10036-y
• Explaining mispredictions of machine learning models using rule induction / Cito, J., Dillig, I., Kim, S., Murali, V., & Chandra, S. (2021). Explaining mispredictions of machine learning models using rule induction. Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. https://doi.org/10.1145/3468264.3468614
• Doing More with Less: Characterizing Dataset Downsampling for AutoML / Zogaj, F., Cambronero, J. P., Rinard, M. C., & Cito, J. (2021). Doing More with Less: Characterizing Dataset Downsampling for AutoML. Proceedings of the VLDB Endowment, 14(11), 2059–2072. https://doi.org/10.14778/3476249.3476262
• Enabling Collaborative Data Science Development with the Ballet Framework / Smith, M. J., Cito, J., Lu, K., & Veeramachaneni, K. (2021). Enabling Collaborative Data Science Development with the Ballet Framework. In J. Nichols (Ed.), Proceedings of the ACM on Human-Computer Interaction (pp. 1–39). Acm Dl. https://doi.org/10.1145/3479575
• Software Engineering for Infrastructure and Configuration (SEConfig) - Workshop Report / Cito, J., & Santolucito, M. (2020). Software Engineering for Infrastructure and Configuration (SEConfig) - Workshop Report. ACM SIGSOFT Software Engineering Notes, 45(2), 23–24. https://doi.org/10.1145/3385678.3385686
• Characterizing Efficiency Optimizations in Solidity Smart Contracts / Brandstätter, T., Schulte, S., Cito, J., & Borkowski, M. (2020). Characterizing Efficiency Optimizations in Solidity Smart Contracts. In 2020 IEEE International Conference on Blockchain (Blockchain). 3rd IEEE International Conference on Blockchain (Blockchain 2020) - Online Conference, Rhodes Island, Greece, EU. IEEE. https://doi.org/10.1109/blockchain50366.2020.00042 / Project: CDL-BOT
• AMS: generating AutoML search spaces from weak specifications / Cambronero, J. P., Cito, J., & Rinard, M. C. (2020). AMS: generating AutoML search spaces from weak specifications. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM Digital Library. https://doi.org/10.1145/3368089.3409700
• An Empirical Study on the Impact of Deimplicitization on Comprehension in Programs Using Application Frameworks / Cito, J., Shen, J., & Rinard, M. (2020). An Empirical Study on the Impact of Deimplicitization on Comprehension in Programs Using Application Frameworks. In Proceedings of the 17th International Conference on Mining Software Repositories. ACM Digital Library. https://doi.org/10.1145/3379597.3387507
• Unified Configuration Setting Access in Configuration Management Systems / Raab, M., Denner, B., Hahnenberg, S., & Cito, J. (2020). Unified Configuration Setting Access in Configuration Management Systems. In Proceedings of the 28th International Conference on Program Comprehension. ACM Digital Library. https://doi.org/10.1145/3387904.3389257

Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.

• Bayesian inference for inverse software performance problems / Fischer, F. (2023). Bayesian inference for inverse software performance problems [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.94660
• Causal discovery for metric-based root cause analysis in application performance monitoring / Siegl, S. (2022). Causal discovery for metric-based root cause analysis in application performance monitoring [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.97982
• Machine learning for interactive performance prediction / Böck, M. (2022). Machine learning for interactive performance prediction [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.89203