Interactive Programming & Analysis Lab
We see the elements of play and exploration in activities of software and data engineering as an essential part of developing larger computational systems.We can broadly view the work we do as part of two categories:
- Empirical Understanding: We we want to understand the underlying structures and artifacts that govern exploration and experimentation in computational tasks.
- System Building: We design, build, and evaluate interventions in the form of systems that either automate or augment human ability to better deal with these structures and artifacts.
Student projects
We have multiple project openings in the areas of interactive programming, machine learning for software engineering, and program synthesis. Please reach out to Jürgen Cito or Michael Schröder Have a look at some potential areas and topics here: IPA Student Project SlidesRecent News
- Students at TU Wien: We are offering various student projects starting spring term 2022 (bachelor's and master's theses, projects in computer science, seminars)
- We are offering two seminars at TU Wien in WS2021/22: AI Seminar on Probabilistic Programming, and Scientific Research and Writing
- Our research on continuous and incremental performance modeling has received a Facebook Research Award
- Our research on synthesizing security patches using machine learning has received an IBM Research Award
Contact
Jürgen Cito (PI)
Assistant Professor (TU Wien)Visiting Research Scientist (Facebook)
Email: juergen.cito@tuwien.ac.at
Google Scholar | GitHub | Twitter
Selected Publications
- Explaining mispredictions of machine learning models using rule induction, Jürgen Cito, Işıl Dillig, Seohyun Kim, Vijayaraghavan Murali, Satish Chandra FSE'21. [pdf]
- Enabling collaborative data science development with the Ballet framework, Micah J. Smith, Jürgen Cito, Kelvin Lu, Kalyan Veeramachaneni CSCW'21. [pdf]
- Doing More with Less: Characterizing Dataset Downsampling for AutoML, Fatjon Zogaj, José Pablo Cambronero, Martin Rinard, Jürgen Cito VLDB'21. [pdf]
- AMS: generating AutoML search spaces from weak specifications, José Pablo Cambronero, Jürgen Cito, Martin Rinard FSE'20. [pdf]
- Interactive Production Performance Feedback in the IDE, Jürgen Cito, Philipp Leitner, Martin Rinard, Harald C. Gall ICSE'19. [pdf]
- Context-Based Analytics - Establishing Explicit Links between Runtime Traces and Source Code, Jürgen Cito, Fábio Oliveira, Philipp Leitner, Priya Nagpurkar, Harald C. Gall ICSE'17 SEIP. [pdf]
- An Empirical Analysis of the Docker Container Ecosystem on GitHub, Jürgen Cito, Gerald Schermann, Philipp Leitner, Erik Wittern, Sali Zumberi, Harald C. Gall MSR'17. [pdf]
- Patterns in the Chaos a Study of Performance Variation and Predictability in Public IaaS Clouds, Philipp Leitner, Jürgen Cito Transaction on Internet Technology'16. [pdf]