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Outputs (57)

Beyond Syntax: How Do LLMs Understand Code? (2025)
Presentation / Conference Contribution
North, M., Atapour-Abarghouei, A., & Bencomo, N. (2025, April). Beyond Syntax: How Do LLMs Understand Code?. Presented at 2025 IEEE/ACM International Conference on Software Engineering ICSE, Ottawa , Canada

Within software engineering research, Large Language Models (LLMs) are often treated as 'black boxes', with only their inputs and outputs being considered. In this paper, we take a machine interpretability approach to examine how LLMs internally repr... Read More about Beyond Syntax: How Do LLMs Understand Code?.

Surprise! Surprise! Learn and Adapt (2025)
Presentation / Conference Contribution
Samin, H., Walton, D., & Bencomo, N. (2025, May). Surprise! Surprise! Learn and Adapt. Presented at 24th International Conference on Autonomous Agents and Multiagent Systems, Detroit, Michigan, USA

Self-adaptive systems (SAS) adjust their behavior at runtime in response to environmental changes, which are often unpredictable at design time. SAS must make decisions under uncertainty, balancing trade-offs between quality attributes (e.g., cost mi... Read More about Surprise! Surprise! Learn and Adapt.

weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms (2025)
Presentation / Conference Contribution
Bencomo, N., Horrocks, J., Walton, D., & Samin, H. (2025, June). weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms. Presented at BMS Annual Scientific Conference 2025, Chesford Grange, Kenilworth, UK

This work introduces weDecide, an AI/ML-based clinical tool designed to support personalised and shared decision-making (PSDM) for menopause treatment. The tool combines explainable machine learning models with multi-criteria decision-making methods... Read More about weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms.

SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability (2025)
Journal Article
Ignatius, H. T. N., Bahsoon, R., Bencomo, N., & Samin, H. (online). SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability. ACM Transactions on Autonomous and Adaptive Systems, https://doi.org/10.1145/3735643

Non-Functional Requirements (NFRs) play a critical role in driving self-adaptation in software systems. In Self-Adaptive Systems (SAS), satisfying multiple NFRs simultaneously introduces significant complexity, as these requirements often conflict-im... Read More about SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability.

Model‐Driven Engineering for Digital Twins: Opportunities and Challenges (2025)
Journal Article
Michael, J., Cleophas, L., Zschaler, S., Clark, T., Combemale, B., Godfrey, T., Khelladi, D. E., Kulkarni, V., Lehner, D., Rumpe, B., Wimmer, M., Wortmann, A., Ali, S., Barn, B., Barosan, I., Bencomo, N., Bordeleau, F., Grossmann, G., Karsai, G., Kopp, O., …Vangheluwe, H. (online). Model‐Driven Engineering for Digital Twins: Opportunities and Challenges. Systems Engineering, https://doi.org/10.1002/sys.21815

Digital twins are increasingly used across a wide range of industries. Modeling is a key to digital twin development—both when considering the models which a digital twin maintains of its real‐world complement (“models in digital twin”) and when cons... Read More about Model‐Driven Engineering for Digital Twins: Opportunities and Challenges.

Declarative Lifecycle Management in Digital Twins (2024)
Presentation / Conference Contribution
Bencomo, N., Kamburjan, E., Tapia Tarifa, S. L., & Broch-Johnsen, E. (2024, September). Declarative Lifecycle Management in Digital Twins. Presented at 1st International Conference on Engineering Digital Twins (EDTconf 2024), Linz, Austria

Together, a digital twin and its physical counterpart can be seen as a self-adaptive system: the digital twin monitors the physical system, updates its own internal model of the physical system, and adjusts the physical system by means of controllers... Read More about Declarative Lifecycle Management in Digital Twins.

Code Gradients: Towards Automated Traceability of LLM-Generated Code (2024)
Presentation / Conference Contribution
North, M., Atapour-Abarghouei, A., & Bencomo, N. (2024, June). Code Gradients: Towards Automated Traceability of LLM-Generated Code. Presented at 2024 IEEE 32nd International Requirements Engineering Conference (RE), Reykjavik, Iceland

Large language models (LLMs) have recently seen huge growth in capability and usage. Within software engineering, LLMs are increasingly being used by developers to generate code. Code generated by an LLM can be seen essentially a continuous mapping f... Read More about Code Gradients: Towards Automated Traceability of LLM-Generated Code.

Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems (2024)
Presentation / Conference Contribution
Camara, J., Hahner, S., Perez-Palacin, D., Vallecillo, A., Acosta, M., Bencomo, N., Calinescu, R., & Gerasimou, S. (2024, April). Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems. Presented at 2024 IEEE/ACM 19th Symposium on Software Engineering for Adaptive and Self-Managing Systems, Lisbon, Portugal

Sources of uncertainty in adaptive systems are rarely independent, and their interaction can affect the attainment of system goals in unpredictable ways. Despite ample work on “taming” uncertainty, the research community has devoted little attention... Read More about Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems.

Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring (2024)
Presentation / Conference Contribution
Götz, S., Samin, H., & Bencomo, N. (2024, April). Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring. Presented at SEAMS '24: 19th International Conference on Software Engineering for Adaptive and Self-Managing Systems, Lisbon, Portugal

Self-adaptive systems are able to adapt themselves according to changing contextual conditions to ensure a set of predefined objectives (e.g., certain non-functional requirements like reliability) is reached. For this, they perform adaptation actions... Read More about Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring.

Responsible AI governance: A response to UN interim report on governing AI for humanity (2024)
Report
Bencomo, N., Kiden, S., Deshmukh, J., Williams, J., Ramchurn, G., Stein, S., Yazdanpanah, V., Stahl, B., Townsend, B., Maple, C., Vincent, C., Sampson, F., Gilbert, G., Ross, J., Martinez del Rincon, J., Lisinska, J., O’Shea, K., Da Costa Abreu, M., Deb, O., Winter, P., …Iniesta, R. (2024). Responsible AI governance: A response to UN interim report on governing AI for humanity. United Nations

A response to UN interim report on governing AI for humanity