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Releasing a software product often is seen as the end point of the software development process: everything's implemented, time to earn some cash! Of course, quite the opposite is true, since 40 to 80% of software development costs are spent during software maintenance activities after release, i.e., to fix bugs, improve the user experience and add new functionality. In the age of AI, maintenance also involves monitoring ML models for performance degradation (drift) and managing technical debt in complex engineering pipelines.
MCIS' mission is to help practitioners maintain their AI-powered software systems. For example, how can we detect and mitigate performance degradation (drift) in ML models? How do we manage technical debt in machine learning pipelines? How can we coordinate vulnerability fixes across large-scale software ecosystems? Which AI-product release-readiness checklists should be followed before deployment?
By empirical research on software development process data stored in revision control systems (Git, ...), mailing list archives, bug repositories (Jira, GitHub Issues, ...), and modern model registries like Hugging Face, MCIS addresses these challenges. Typically, the outcome of our research are models developed using data mining, statistical analysis, and advanced machine learning techniques.
Turning textual source code into an installable software product, ready to execute on the user's hardware platform, is at the heart of software construction. Practitioners need help releasing their traditional and AI-powered products faster using optimized CI/CD and Infrastructure-as-Code, significantly shortening commit turn-around time.
MCIS' mission is to help practitioners release their products faster, but without sacrificing quality. To achieve this, one needs insight into the modern release engineering process. For example, how can we reduce redundant continuous integration activity through commit grouping and skip prediction? How do we optimize build batching algorithms at scale? How healthy is our software supply chain, and why do some builds fail to be reproducible across different ecosystems? How can we prepare our development process for seamless continuous delivery?
Using the same empirical techniques as for software maintenance, we address the questions above. In addition, our lab has unique expertise and data sets on release engineering and build systems (Bazel, GitHub Actions, ...), and close contacts with industry for access to "real-life" data.
Software Intelligence forms the heart of MCIS' research. We leverage Large Language Models (LLMs) and software analytics to facilitate program comprehension and development. From boosting LLM-based code translation using transitive intermediate translations to predicting code review hot-spots, we turn software data into actionable developer intelligence.
MCIS leverages AI to help practitioners understand and develop their software systems and infrastructure to facilitate maintenance, construction and other development activities. For example, how can we enhance LLM-based code translation using transitive intermediate translations? Which files in a pull request are most likely to need comments from code reviewers? How do we effectively manage ML assets and navigate foundation model leaderboards?
Intelligence covers elements from advanced model versioning (Semantic Versioning on Hugging Face), large language models for code, and human-AI collaboration. Given that knowledge is crucial in software development, software intelligence is central to MCIS' mission.
Kosei Horikawa, Hao Li (*), Yutaro Kashiwa, Bram Adams, Hajimu Iida and Ahmed E. Hassan (2026). Agentic Refactoring: An Empirical Study of AI Coding Agents, Transactions on Software Engineering and Methodology (TOSEM), ACM, to appear.
Lin Geng (*), Hao Li (*), Sidney Givigi and Bram Adams (2026). SwarmUpdate: Hierarchical Software Updates and Deep Learning Model Patching for Heterogeneous UAV Swarms, Transactions on Software Engineering and Methodology (TOSEM), ACM, to appear.
Worawalan Chatlatanagulchai, Hao Li (*), Yutaro Kashiwa, Brittany Reid, Kundjanasith Thonglek, Pattara Leelaprute, Arnon Rungsawang, Bundit Manaskasemsak, Bram Adams, Ahmed E. Hassan and Hajimu Iida (2026). Agent READMEs: An Empirical Study of Context Files for Agentic Coding, Transactions on Software Engineering and Methodology (TOSEM), ACM, to appear.
Mohammed Mehedi Hasan (*), Hao Li (*), Emad Fallahzadeh (*), Gopi Krishnan Rajbahadur, Bram Adams and Ahmed E. Hassan (2026). Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers, Transactions on Software Engineering and Methodology (TOSEM), ACM, to appear.