Dr. Christian Kästner
Associate Professor; Director, Software Engineering Ph.D. program
Bio
I am an associate professor in the School of Computer Science at Carnegie Mellon University. My current interests are in software engineering for software systems with ML components (or teaching software engineering to data scientists, "machine learning in production"), open-source sustainability, and software-supply-chain security.
I am generally interested in understanding the limits of modularity and complexity caused by variability in software systems, which naturally brings me to questions of quality assurance, interoperability, and feature interactions. My research combines rigorous empirical research with program analysis and tool building.
Research
Research Interests
- Analysis & Assurance
- Developer Tools
- Privacy and Security
- Software Data Analysis
Software Engineering for AI-Enabled Systems
We explore how different facets of software engineering change with the introduction of machine learning components in production systems, with an interest in interdisciplinary collaboration, quality assurance, system-level thinking, safety, and better data science tools: Capturing Software Engineering for AI-Enabled Systems · Interdisciplinary Collaboration in Engineering AI-Enabled Systems · Developer Tooling for Data Scientists
Sustainability and Fairness in Open Source
We study the dynamics of open source communities with a focus on unstanding and fostering fair and sustainable environments. Primarily with empirical research methods, we explore topics, such as open source culture, coordination, stress and disengagement, funding, and security: Sustainability and Fairness in Open Source · Collaboration and Coordination in Open Source · Adoption of Practices and Tooling
Quality Assurance for Highly-Configurable Software Systems
We explore approaches to scale quality assurance strategies, including parsing, type checking, data-flow analysis, and testing, to huge configuration spaces in order to find variability bugs and detect feature interactions: Variational Analysis · Analysis of Unpreprocessed C Code · Variational Type Checking and Data-Flow Analysis · Variational Execution (Testing) · Sampling · Feature Interactions · Variational Specifications · Assuring and Understanding Quality Attributes as Performance and Energy · Security
Publications
Christian Kästner. Machine Learning in Production: From Models to Products. 2022. (a final copy will be published late 2024 or 2025 by MIT Press). [ http, bib ]
Chenyang Yang, , , , and Christian Kästner. What Is Wrong with My Model? Identifying Systematic Problems with Semantic Data Slicing. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE), Los Alamitos, CA: IEEE Computer Society, November 2024. Acceptance rate: 26 % (155/587). [ bib ]
, , Nadia Nahar, , , and Christian Kästner. Lessons from Clinical Communications for AI Systems. In Proceedings of the AAAI Conference on AI, Ethics, and Society (AIES), October 2024. [ .pdf, bib ]
Nadia Nahar, , , Shurui Zhou, and Christian Kästner. The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products. In Proceedings of the 47th International Conference on Software Engineering (ICSE), April 2025. [ .pdf, bib ]
Courtney Miller, , , Bogdan Vasilescu, and Christian Kästner. Understanding the Response to Open-Source Dependency Abandonment in the npm Ecosystem. In Proceedings of the 47th International Conference on Software Engineering (ICSE), April 2025. [ .pdf, bib ]
Nadia Nahar, , , , , , and Christian Kästner. Regulating Explainability in Machine Learning Applications -- Observations from a Policy Design Experiment. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT), pages 2101--2112, June 2024. [ .pdf, doi, bib ]