Jane Cleland‐Huang
Papers
10
Total Citations
167
H-Index
6
About
Jane Cleland-Huang is a leading researcher in software engineering for safety-critical, autonomous, and cyber-physical systems (CPS). Her work is foundational in requirements engineering, traceability, and runtime monitoring, with a strong focus on enabling effective human-machine teaming (HMT). She has pioneered methods for creating transparent, explainable autonomous systems, particularly for small Unmanned Aerial Systems (sUAS) operating under the MAPE-K feedback loop. Her major contributions include developing trace queries for safety requirements in high-assurance systems (42 citations), extending the MAPE-K model to support richer human-autonomy partnerships (28 citations), and advancing explainability for human-on-the-loop swarms (20 citations). She also introduced timely traceability recommendations to break the "big-bang" practice of late-stage link creation (17 citations), and created AMon, a domain-specific language for adaptive monitoring of CPS (11 citations). With over 160 total citations across her top works, Cleland-Huang’s research directly addresses the engineering challenges of integrating AI-supported computer vision and runtime monitoring into autonomous vehicles and robots. Her work is essential for students and researchers interested in building trustworthy, adaptive, and human-centered autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Trace Queries for Safety Requirements in High Assurance Systems42 citations · 2012
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- 3Extending MAPE-K to support human-machine teaming28 citations · 2022
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- 7Towards flexible runtime monitoring support for ROS-based applications6 citations · 2022
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- 10SAFA: A Tool for Supporting Safety Analysis in Evolving Software Systems3 citations · 2022