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Jürgen Cito

Associate Prof. Dipl.-Ing. Dr.sc. / BSc

Jürgen Cito

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2024W

2025S

 

  • Language-Agnostic Static Analysis of Probabilistic Programs / Böck, M., Schröder, M., & Cito, J. (2024). Language-Agnostic Static Analysis of Probabilistic Programs. In ASE ’24: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (pp. 78–90). Association for Computing Machinery. https://doi.org/10.1145/3691620.3695031
  • Understanding Hackers’ Work: An Empirical Study of Offensive Security Practitioners / Happe, A., & Cito, J. (2023). Understanding Hackers’ Work: An Empirical Study of Offensive Security Practitioners. In ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1669–1680). Association for Computing Machinery. https://doi.org/10.1145/3611643.3613900
  • Getting pwn’d by AI: Penetration Testing with Large Language Models / Happe, A., & Jürgen, C. (2023). Getting pwn’d by AI: Penetration Testing with Large Language Models. In ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 2082–2086). Association for Computing Machinery. https://doi.org/10.1145/3611643.3613083
  • 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)
  • Performance Prediction From Source Code Is Task and Domain Specific / Böck, M., Habchi, S., Nayrolles, M., & Cito, J. (2023). Performance Prediction From Source Code Is Task and Domain Specific. In 2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) (pp. 35–42). IEEE. https://doi.org/10.1109/ICPC58990.2023.00015
  • 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)
  • 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
  • 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
  • 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. IEEE. https://doi.org/10.1109/blockchain50366.2020.00042
    Project: CDL-BOT (2020–2022)
  • 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
  • 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
  • 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
  • Identifying Web Performance Degradations through Synthetic and Real-User Monitoring / Cito, J., Gotowka, D., Leitner, P., Pelette, R., Suljoti, D., & Dustdar, S. (2015). Identifying Web Performance Degradations through Synthetic and Real-User Monitoring. Journal of Web Engineering, 14(No. 5 & amp;6), 414–442. http://hdl.handle.net/20.500.12708/151174
  • Identifying Root Causes of Web Performance Degradation Using Changepoint Analysis / Cito, J., Suljoti, D., Leitner, P., & Dustdar, S. (2014). Identifying Root Causes of Web Performance Degradation Using Changepoint Analysis. In S. Casteleyn, G. Rossi, & M. Winckler (Eds.), Web Engineering : 14th International Conference, ICWE 2014, Toulouse, France, July 1-4, 2014, Proceedings (pp. 181–199). Springer International Publishing. https://doi.org/10.1007/978-3-319-08245-5_11
  • Statistical methods in managing web performance / Cito, J. (2014). Statistical methods in managing web performance [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2014.21879
    Download: PDF (2.35 MB)