Staff profile

Looking for a PhD ?

(Slowly, I will be moving this website to https://nellybencomo.webspace.durham.ac.uk/ but, not yet!)

Potential new PhD students: having good PhD students can be super rewarding! I am always interested in receiving new good PhD Students. Contact me to talk about your research proposals, academic visits or scholarships.PhD Funding Opportunities: 

---> There are always funding opportunities offered (in Durham and other sources. <--- It is about being ready for when they appear). Therefore, a good step is to apply for a PhD as soon as possible. Contact me if you want to get the instructions to start your application.

If you bring your own research funding, we can chat (contact me if interested)

Research and Biography

Publications: Google Scholar citations  DBLP  Scopus  Social Media: Twitter @nellybencomo 

Funding: EPSRC Funding

Bio: I exploit the interdisciplinary aspects of software engineering, which encompass both technical and human concerns, while developing techniques for intelligent, autonomous, and highly distributed systems.

I am an Associate Professor in the CS Department at Durham University. Earlier, I was a Senior Lecturer (equiv. Associate Professor) in the Computer Science Department at Aston University. From September 2020 I am the PI of the EPSRC Twenty20Insight research project. In 2019 I was granted the Leverhulme Fellowship "QuantUn: quantification of uncertainty using Bayesian surprises." Before, I was a Marie Curie Fellow at INRIA Paris - Roquencourt. The Marie Curie project is called Requirements-aware Systems (nickname: Requirements@run.time). Before, that I was a Senior Research Associate at Lancaster University (UK) where I also got my PhD.

Research Projects (Current and Recent):

-  Durham's Strategic Research Fund (SRF) JusTN0W: Just Transitions to a Net Zero World (2024 -  Dec 2028) Principal Investigators (PIs): Professor Petra Minnerop (PI, Law), Professor Laura Marsiliani (PI, Bussiness School), Dr John Bothwell (PI, Bioenergy), Dr Nelly Bencomo (PI, Computer Science Department) ~£5 Million

-  Leverhulme Centre for Algorithmic Life (CAL) PI Prof Louise Amoore (Department of Geography). I am the Project Lead in the Department of Computer Science.

- EPSRC IAA: weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms, Principal Investigator (PI) Nelly Bencomo, £120 K (Jan 2024 - May 2025)

- UArctic network Project DART (Digital Arctic Twin) (2025-27) is a networking project in the context of JusTN0W. Our DART (Digital ARktisk Tvilling, Digital Arctic Twin) project will develop digital twins to study transitions to net zero under the aegis of the UArctic ‘Climate Justice’ thematic network. Partners: Oslo University (Norway), Durham University (UK), IT University and Aarhus University (Denmark), Glasgow Caledonian (UK),  University of Washington, USA.

- EPSRC Research Twenty20Insight (EPSRC link) (2020-2024) Principal Investigator (PI) Nelly Bencomo, £892,384 (total Value to Aston and then Durham £587 K) website Twenty20Insight

- Project: "Research Culture Enrichment for Women at Work" 2024-25. (PI N Bencomo, co-I  Barbara Bechter). Funded by the Flourish@Durham programme, which enables faculty and departmental strategies for research culture to develop across Durham university. 

- ALTS: AI-enabled Legal Technologies and their Consequences on Society (PI N Bencomo, Co-PIs W Lucy and M Hardey)(2023-24). Partially funded by the Institute of Advance Studies and the EPSRC.

– iDecide: Quantifying Uncertainty in Models using Artificial Intelligence for Personalised and Shared Decision-Making in Digital Health. (Jun- Aug 2023)

– ERiMA: Envisioning Risk Models for Assessment of AI-based applications (Sept-Nov 2023)

- Research Fellowship by Leverhulme Trust “QuantUn: Quantification of Uncertainty using Bayesian Surprises” (2019-2020)

– ReqModAI Requirements Models for Artificial Intelligence: Framework and Casestudy (Jul- Nov 2022)

- Marie Curie Action Intra-European Fellowship requirements@runtime: Requirements-aware Systems, €222.547, Inria, France, (2011-2013)

- 2 VC international PhD scholarships, Aston University, (2018)

- Faculty PhD scholarship, Aston University (2015)

- Visiting Scholar Funding 2015, Aston University

- M@TURE Models @ run Time for self-adaptive pervasive systems: enabling User-in-the-loop, REquirement-awareness, and interoperability in ad hoc settings, 24-month, Inria-funded international collaboration grant (Brazil-France). We also won a second version (2014-2016) 

Awards / Rewards / Recognitions:

- Best Paper Award International Conference on Engineering Digital Twins 2024 (EDTconf'24)  “Declarative Lifecycle Management in Digital Twins”, E Kamburjan, N Bencomo, S Lizeth Tapia Tarifa, E Broch-Johnsen 

- Best Software Artefact at  Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS’24),  "Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring" by S Götz, H Samin, N Bencomo

- I am on the list of Women for Innovation in Health Science in the North of England by the Northern Health Science Alliance (NHSA) and @N8research (just 3 Women from CS are part of the list), 8th March 2024 https://www.thenhsa.co.uk/2024/03/northinnovationwomen-2024-list-revealed/

- 10-year Most Influential Paper Award 2019, "RELAX: Incorporating Uncertainty into the Specification of Self-Adaptive Systems", by Jon Whittle, Pete Sawyer, Nelly Bencomo, Betty H.C. Cheng, Jean-Michel Bruel, Proceedings of 17th IEEE International Requirements Engineering Conference (RE09), pp. 7988, September 2009, Atlanta, Georgia.

- 10-year Most Influential Paper Award 2019, "A Goal-Based Modeling Approach to Develop Requirements of an Adaptive System with Environmental Uncertainty" by Betty H.C. Cheng, Pete Sawyer, Nelly Bencomo, Jon Whittle, in the Proceedings of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MoDELS 2009), pp. 468483, Denver, Colorado, 2009.

- Best Paper Award REFSQ 2013: "Supporting Decision-making for Self-adaptive Systems: From Goal Models to Dynamic Decision Networks" by N. Bencomo, A. Belaggoun

Books

Models@run.time: Foundations, Applications, and Roadmaps - Programming and Software Engineering (2014) Editors: Nelly Bencomo (editor), Robert B. France (editor), Betty H.C. Cheng (editor), Uwe Aßmann (editor)

https://link.springer.com/book/10.1007/978-3-319-08915-7 

https://www.amazon.com/Models-run-time-Foundations-Applications-Roadmaps/dp/3319089145

Miscellaneous

In other words, what is really important!

In Durham, I am planning to enjoy Durham County, Scotland and the Lake District, among other beautiful places in the North and East of the UK :)

During my time in Birmingham, I was not working. I liked walking on the hills (the Malvern Hills, Snowdonia), running, dancing, taking pictures, hanging around with my family and friends, and Talisker, my dog. I enjoy running by the canal in Birmingham. I wish I could have more time for these activities.

During my time in Paris, I used to go to Museums & Cafes :) if my daughter did not decide something else. I also did some exercise with my dog, running near Inria and by the river Seine in Paris. Happy days!

I love eating good food, and I am MAD about sushi. I LOVE chocolate; BTW, dark and bitter chocolate; if it is not at least 75% Cocoa, that is not chocolate :). If chocolate is from Venezuela, that is even better!

 

Previous PhD Students

- Dr Owen Reynold, since January 2019, Submitted 2023 (co-supervision with Antonio Garcia). Now a researcher in York University.

- Dr Huma Samin Graduated 2022. Now a Lecturer at Exeter University

- Dr Juan Marcelo Parra, Graduated 2022.  Senior Researcher at Bristol University (2022-25). Now working in BT.

- Dr Luis Hernan Garcia Paucar, PhD, Graduated in March 2020. Now a Senior Teaching Fellow at Aston University

- Dr Amel Belaggoun, Master Universite de Versailles Saint-Quentin-en-Yvelines and TELECOM SudParis and Inria, France, Graduated 2013

• AI/ML
• Decision-making under Uncertainty
• Software Engineering for Autonomous and Self-adaptive Systems
• Software Engineering for AI/ML Systems
• Autonomous Systems
• runtime models

• 2025: PC Co-Chair Conference on Engineering Digital Twins (EDTconf 2026):
• 2025: Keynote (ICPRAM 2025): Bayesian Theory of Surprise to Quantify Degrees of Uncertainty: Keynote Title: Bayesian Theory of Surprise to Quantify Degrees of Uncertainty Keynote Lecturer: Nelly Bencomo Presented on: 25/02/2025, Porto, Portugal
• 2024: Keynote (Modellierung 2024 ): Models for Human-Machine Teaming for Shared Decision-Making under Uncertainty: https://bpt.hpi.uni-potsdam.de/modellierung2024/keynotes/
• 2024: ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Issues Associate Editors and Advisors: https://dl.acm.org/journal/taas/editorial-board
• 2022: PC Co-Chair MODELS 2022:
• 2022: ACM Distinguished Speaker - https://speakers.acm.org/speakers/bencomo_18448:
• 2020: Keynote (VaMOS 2020): Next Steps in Variability Management due to Autonomous Behaviour and Runtime Learning:
• 2020: Member of the IEEE TCSE Executive Committee TCSE (22-26): I was elected again to be part of the IEEE TCSE Executive Committee
  
  
• 2020: Member of the IEEE TCSE: Member of the IEEE TCSE (Technical Council on Software Engineering) members-at-large (TCSE Elected Officers 2020- )
• 2019: Member of Editor Board of SoSym: Member of Editor Board of Software and Systems Modeling (2019- )
• 2017: Keynote (ICAC 2017): The Role of models@run.time in Autonomic Systems:

Conference Paper

• Surprise! Surprise! Learn and Adapt
  
  Samin, H., Walton, D., & Bencomo, N. (in press). Surprise! Surprise! Learn and Adapt (Y. Vorobeychik, S. Das, & A. Nowé, Eds.).
• weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms
  
  Bencomo, N., Horrocks, J., Walton, D., & Samin, H. (in press). weDecide: Clinical Tool for Shared Decision-Making for Treatment of Menopause Symptoms. Post Reproductive Health Journal [Conference abstract].
• Beyond Syntax: How Do LLMs Understand Code?
  
  North, M., Atapour-Abarghouei, A., & Bencomo, N. (in press). Beyond Syntax: How Do LLMs Understand Code?. Presented at 2025 IEEE/ACM International Conference on Software Engineering ICSE, Ottawa , Canada.
• Declarative Lifecycle Management in Digital Twins
  
  Bencomo, N., Kamburjan, E., Tapia Tarifa, S. L., & Broch-Johnsen, E. (2024, October 31). Declarative Lifecycle Management in Digital Twins. Presented at 1st International Conference on Engineering Digital Twins (EDTconf 2024), Linz, Austria. https://doi.org/10.1145/3652620.3688248
• Code Gradients: Towards Automated Traceability of LLM-Generated Code
  
  North, M., Atapour-Abarghouei, A., & Bencomo, N. (2024). Code Gradients: Towards Automated Traceability of LLM-Generated Code. In 2024 IEEE 32nd International Requirements Engineering Conference (RE) (pp. 321-329). IEEE. https://doi.org/10.1109/RE59067.2024.00038
• Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems
  
  Camara, J., Hahner, S., Perez-Palacin, D., Vallecillo, A., Acosta, M., Bencomo, N., Calinescu, R., & Gerasimou, S. (2024). Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems. In 2024 IEEE/ACM 19th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE. https://doi.org/10.1145/3643915.3644084
• Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring
  
  Götz, S., Samin, H., & Bencomo, N. (2024). Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring. In SEAMS ’24: Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. IEEE/ACM. https://doi.org/10.1145/3643915.3644106
• Automated Provenance Collection at Runtime as a Cross-Cutting Concern
  
  James Reynolds, O., García-Domínguez, A., & Bencomo, N. (2023). Automated Provenance Collection at Runtime as a Cross-Cutting Concern. In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (pp. 276-285). IEEE. https://doi.org/10.1109/MODELS-C59198.2023.00057
• History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems
  
  Parra-Ullauri, J., Garcia-Dominguez, A., Bencomo, N., & Garcia Paucar, L. (2022, October). History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems. Presented at MODELS ’22: 25th International Conference on Model Driven Engineering Languages and Systems, Montreal, Canada. https://doi.org/10.1145/3550356.3561538
• The Implications of ‘Soft’ Requirements
  
  Sutcliffe, A., Sawyer, P., & Bencomo, N. (2022). The Implications of ‘Soft’ Requirements. In E. Knauss, G. Mussbacher, C. Arora, M. Bano, & J.-G. Schneider (Eds.), 2022 IEEE 30th International Requirements Engineering Conference (RE) (pp. 178-188). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/re54965.2022.00022
• Decision-Making Support for Adaptive Learning Management Systems based on Bayesian Inference
  
  Bencomo, N., Samin, H., & Pavlich-Mariscal, J. (2022, July 27 – 2022, July 31). Decision-Making Support for Adaptive Learning Management Systems based on Bayesian Inference [Conference paper]. Presented at CausalEDM’22, Durham.
• A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHS COVID-19 Test and Trace App in England and Wales
  
  Garcia-Paucar, L., Bencomo, N., Sutcliffe, A., & Sawyer, P. (2022, April). A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHS COVID-19 Test and Trace App in England and Wales. Presented at 37th Annual ACM Symposium on Applied Computing (ACM SAC’2022), Brno, Czech Republic. https://doi.org/10.1145/3477314.3507147
• Investigating the Potential Impact of Values on Requirements and Software Engineering
  
  Sutcliffe, A. G., Sawyer, P., Liu, W., & Bencomo, N. (2021). Investigating the Potential Impact of Values on Requirements and Software Engineering. Presented at 43rd IEEE/ACM International Conference on Software Engineering: Software Engineering in Society, ICSE (SEIS) 2021, Madrid, Spain, May 25-28, 2021. https://doi.org/10.1109/icse-seis52602.2021.00013
• Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead
  
  Bucchiarone, A., Cicchetti, A., Bencomo, N., Loria, E., & Marconi, A. (2021). Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead. Presented at 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2021, Madrid, Spain, May 18-24, 2021. https://doi.org/10.1109/seams51251.2021.00028
• RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation
  
  Samin, H., Paucar, L. H. G., Bencomo, N., Hurtado, C. M. C., & Fredericks, E. M. (2021). RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation. Presented at 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2021, Madrid, Spain, May 18-24, 2021. https://doi.org/10.1109/seams51251.2021.00039
• Towards priority-awareness in autonomous intelligent systems
  
  Samin, H., Paucar, L. H. G., Bencomo, N., & Sawyer, P. (2021). Towards priority-awareness in autonomous intelligent systems (C.-C. Hung, J. Hong, A. Bechini, & E. Song, Eds.). ACM. https://doi.org/10.1145/3412841.3442007
• Towards an architecture integrating complex event processing and temporal graphs for service monitoring
  
  Ullauri, J. M. P., García-Domínguez, A., Boubeta-Puig, J., Bencomo, N., & Ortiz, G. (2021). Towards an architecture integrating complex event processing and temporal graphs for service monitoring (C.-C. Hung, J. Hong, A. Bechini, & E. Song, Eds.). ACM. https://doi.org/10.1145/3412841.3441923
• Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App
  
  Paucar, L. H. G., Bencomo, N., Sutcliffe, A. G., Yue, T., & Mirakhorli, M. (2021). Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App (T. Yue & M. Mirakhorli, Eds.). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/rew53955.2021.00026
• Pri-AwaRE: Tool Support for priority-aware decision-making under uncertainty
  
  Samin, H., Bencomo, N., & Sawyer, P. (2021). Pri-AwaRE: Tool Support for priority-aware decision-making under uncertainty. Presented at 29th IEEE International Requirements Engineering Conference, RE 2021, Notre Dame, IN, USA, September 20-24, 2021. https://doi.org/10.1109/re51729.2021.00065
• Eigth International Workshop on Artificial Intelligence and Requirements Engineering (AIRE'21)
  
  Bencomo, N., Harrison, R., & Rodríguez, D. (2021). Eigth International Workshop on Artificial Intelligence and Requirements Engineering (AIRE’21) (T. Yue & M. Mirakhorli, Eds.). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/rew53955.2021.00015
• From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems
  
  Ullauri, J. M. P., García-Domínguez, A., & Bencomo, N. (2021). From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems. Presented at ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS 2021 Companion, Fukuoka, Japan, October 10-15, 2021. https://doi.org/10.1109/models-c53483.2021.00127
• Modeling Autonomic Systems in the time of ML, DevOps and Microservices
  
  Bencomo, N. (2021). Modeling Autonomic Systems in the time of ML, DevOps and Microservices. Presented at ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS 2021 Companion, Fukuoka, Japan, October 10-15, 2021. https://doi.org/10.1109/models-c53483.2021.00118
• Next steps in variability management due to autonomous behaviour and runtime learning
  
  Bencomo, N. (2020). Next steps in variability management due to autonomous behaviour and runtime learning (M. Cordy, M. Acher, D. Beuche, & G. Saake, Eds.). ACM. https://doi.org/10.1145/3377024.3380451
• Towards an assessment grid for intelligent modeling assistance
  
  Mussbacher, G., Combemale, B., Abrahão, S., Bencomo, N., Burgueño, L., Engels, G., Kienzle, J., Kühn, T., Mosser, S., Sahraoui, H. A., Weyssow, M., Guerra, E., & Iovino, L. (2020). Towards an assessment grid for intelligent modeling assistance (E. Guerra & L. Iovino, Eds.). ACM. https://doi.org/10.1145/3417990.3421396
• Temporal Models for History-Aware Explainability
  
  Ullauri, J. M. P., García-Domínguez, A., Paucar, L. H. G., & Bencomo, N. (2020). Temporal Models for History-Aware Explainability (A. Gherbi, W. Hamou-Lhadj, & A. Bali, Eds.). ACM. https://doi.org/10.1145/3419804.3420276
• Run-time and Collective Adaptation of Gameful Systems
  
  Bucchiarone, A., Bencomo, N., Loria, E., Marconi, A., & Cicchetti, A. (2020). Run-time and Collective Adaptation of Gameful Systems. Presented at 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2020, Companion Volume, Washington, DC, USA, August 17-21, 2020. https://doi.org/10.1109/acsos-c51401.2020.00046
• Automated provenance graphs for models@run.time
  
  Reynolds, O., García-Domínguez, A., & Bencomo, N. (2020). Automated provenance graphs for models@run.time (E. Guerra & L. Iovino, Eds.). ACM. https://doi.org/10.1145/3417990.3419503
• Towards History-Aware Self-Adaptation with Explanation Capabilities
  
  García-Domínguez, A., Bencomo, N., Ullauri, J. M. P., & Paucar, L. H. G. (2019). Towards History-Aware Self-Adaptation with Explanation Capabilities. Presented at IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W@SASO/ICCAC 2019, Umea, Sweden, June 16-20, 2019. https://doi.org/10.1109/fas-w.2019.00018
• ARRoW: automatic runtime reappraisal of weights for self-adaptation
  
  Paucar, L. H. G., Bencomo, N., & Yuen, K. K. F. (2019). ARRoW: automatic runtime reappraisal of weights for self-adaptation (C.-C. Hung & G. A. Papadopoulos, Eds.). ACM. https://doi.org/10.1145/3297280.3299743
• An architectural framework for quality-driven adaptive continuous experimentation
  
  Jiménez, M. A., Rivera, L. F., Villegas, N. M., Tamura, G., Müller, H. A., & Bencomo, N. (2019). An architectural framework for quality-driven adaptive continuous experimentation. Presented at Proceedings of the Joint 4th International Workshop on Rapid Continuous Software Engineering and 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution, RCoSE-DDrEE@ICSE 2019, Montreal, QC, Canada, May 27, 2019. https://doi.org/10.1109/rcose/ddree.2019.00012
• RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning
  
  Bencomo, N., & Paucar, L. H. G. (2019). RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (M. Kessentini, T. Yue, A. Pretschner, S. Voss, & L. Burgueño, Eds.). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/models.2019.00005
• Querying and Annotating Model Histories with Time-Aware Patterns
  
  García-Domínguez, A., Bencomo, N., Ullauri, J. M. P., & Paucar, L. H. G. (2019). Querying and Annotating Model Histories with Time-Aware Patterns (M. Kessentini, T. Yue, A. Pretschner, S. Voss, & L. Burgueño, Eds.). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/models.2019.000-2
• Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes
  
  Paucar, L. H. G., & Bencomo, N. (2019). Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. Presented at 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019, Umea, Sweden, June 16-20, 2019. https://doi.org/10.1109/saso.2019.00011
• Preface to 9th International Workshop on Model-Driven Requirements Engineering
  
  Bencomo, N., Mussbacher, G., Moreira, A., Araújo, J., & Sánchez, P. (2019). Preface to 9th International Workshop on Model-Driven Requirements Engineering. Presented at 27th IEEE International Requirements Engineering Conference Workshops, RE 2019 Workshops, Jeju Island, Korea (South), September 23-27, 2019. https://doi.org/10.1109/rew.2019.00009
• Addressing the QoS drift in specification models of self-adaptive service-based systems
  
  Torres, R., Bencomo, N., & Astudillo, H. (2013). Addressing the QoS drift in specification models of self-adaptive service-based systems. Presented at 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013, San Francisco, CA, USA, May 25-26, 2013. https://doi.org/10.1109/raise.2013.6615201
• Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks
  
  Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. Presented at 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, RAISE 2013, San Francisco, CA, USA, May 25-26, 2013. https://doi.org/10.1109/raise.2013.6615198
• Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks
  
  Bencomo, N., & Belaggoun, A. (2013). Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks. In J. Dörr & A. L. Opdahl (Eds.), Lecture Notes in Computer Science (pp. 221-236). Springer Verlag. https://doi.org/10.1007/978-3-642-37422-7_16
• Dynamic decision networks for decision-making in self-adaptive systems: a case study
  
  Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Dynamic decision networks for decision-making in self-adaptive systems: a case study (M. Litoiu & J. Mylopoulos, Eds.). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/seams.2013.6595498
• Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances
  
  Lemos, R. de, Garlan, D., Ghezzi, C., Giese, H., Andersson, J., Litoiu, M., Schmerl, B. R., Weyns, D., Baresi, L., Bencomo, N., Brun, Y., Cámara, J., Calinescu, R., Cohen, M. B., Gorla, A., Grassi, V., Grunske, L., Inverardi, P., Jézéquel, J.-M., … Zambonelli, F. (2013). Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances. In R. de Lemos, D. Garlan, C. Ghezzi, & H. Giese (Eds.), Lecture Notes in Computer Science (pp. 3-30). Springer Verlag. https://doi.org/10.1007/978-3-319-74183-3_1

Journal Article

• SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability
  
  Ignatius, H. T. N., Bahsoon, R., Bencomo, N., & Samin, H. (in press). SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability. ACM Transactions on Autonomous and Adaptive Systems (TAAS).
• Model‐Driven Engineering for Digital Twins: Opportunities and Challenges
  
  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., … Vangheluwe, H. (2025). Model‐Driven Engineering for Digital Twins: Opportunities and Challenges. Systems Engineering. Advance online publication. https://doi.org/10.1002/sys.21815
• Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements
  
  Garcia, L., Samin, H., & Bencomo, N. (2024). Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements. ACM Transactions on Autonomous and Adaptive Systems, 19(2), Article 11. https://doi.org/10.1145/3643889
• To download or not to download the Covid-19 Track and Trace App? What is more influential in users’ minds?
  
  Sutcliffe, A., Bencomo, N., Darby, A., Paucar, L. H., & Sawyer, P. (2023). To download or not to download the Covid-19 Track and Trace App? What is more influential in users’ minds?. International Journal of Human-Computer Studies, 180, Article 103140. https://doi.org/10.1016/j.ijhcs.2023.103140
• Decision-making under uncertainty: be aware of your priorities
  
  Samin, H., Bencomo, N., & Sawyer, P. (2022). Decision-making under uncertainty: be aware of your priorities. Software and Systems Modeling, 21(6), 2213-2242. https://doi.org/10.1007/s10270-021-00956-0
• The Uncertainty Interaction Problem in Self-Adaptive Systems
  
  Camara, J., Troya1, J., Vallecillo, A., Bencomo, N., Calinescu, R., Cheng, B., Garlan, D., & Schmerl, B. (2022). The Uncertainty Interaction Problem in Self-Adaptive Systems. Software and Systems Modeling, 21(4), 1277-1294. https://doi.org/10.1007/s10270-022-01037-6
• Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning
  
  Parra-Ullauri, J. M., Garcıa-Domınguez, A., Bencomo, N., Zheng, C., Zhen, C., Boubeta-Puig, J., Ortiz, G., & Yang, S. (2022). Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning. Software and Systems Modeling, 21(3), 1091-1113. https://doi.org/10.1007/s10270-021-00952-4
• The Secret to Better AI and Better Software (Is Requirements Engineering)
  
  Bencomo, N., Guo, J., Harrison, R., Heyn, H.-M., & Menzies, T. (2022). The Secret to Better AI and Better Software (Is Requirements Engineering). IEEE Software, 39(1), 105-110. https://doi.org/10.1109/ms.2021.3118099
• Cronista: a multi-database automated provenance collection system for runtime models
  
  Reynolds, O., García-Domínguez, A., & Bencomo, N. (2022). Cronista: a multi-database automated provenance collection system for runtime models. Information and Software Technology, 141, Article 106694. https://doi.org/10.1016/j.infsof.2021.106694
• A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems
  
  Combemale, B., Kienzle, J., Mussbacher, G., Ali, H., Amyot, D., Bagherzadeh, M., Batot, E., Bencomo, N., Benni, B., Bruel, J.-M., Cabot, J., Cheng, B. H., Collet, P., Engels, G., Heinrich, R., Jézéquel, J.-M., Koziolek, A., Mosser, S., Reussner, R. H., … Wimmer, M. (2021). A Hitchhiker’s Guide to Model-Driven Engineering for Data-Centric Systems. IEEE Software, 38(4), 71-84. https://doi.org/10.1109/ms.2020.2995125
• Reward-Reinforced Reinforcement Learning for Multi-agent Systems
  
  Zheng, C., Yang, S., Ullauri, J. M. P., García-Domínguez, A., & Bencomo, N. (2021). Reward-Reinforced Reinforcement Learning for Multi-agent Systems. CoRR, abs/2103.12192.
• Agent-Based Framework for Self-Organization of Collective and Autonomous Shuttle Fleets
  
  Bucchiarone, A., Sanctis, M. D., & Bencomo, N. (2021). Agent-Based Framework for Self-Organization of Collective and Autonomous Shuttle Fleets. IEEE Trans. Intell. Transp. Syst., 22(6), 3631-3643. https://doi.org/10.1109/tits.2020.3021592
• Opportunities in intelligent modeling assistance
  
  Mussbacher, G., Combemale, B., Kienzle, J., Abrahão, S., Ali, H., Bencomo, N., Búr, M., Burgueño, L., Engels, G., Jeanjean, P., Jézéquel, J.-M., Kühn, T., Mosser, S., Sahraoui, H. A., Syriani, E., Varró, D., & Weyssow, M. (2020). Opportunities in intelligent modeling assistance. Softw. Syst. Model., 19(5), 1045-1053. https://doi.org/10.1007/s10270-020-00814-5
• Toward model-driven sustainability evaluation
  
  Kienzle, J., Mussbacher, G., Combemale, B., Bastin, L., Bencomo, N., Bruel, J.-M., Becker, C., Betz, S., Chitchyan, R., Cheng, B. H., Klingert, S., Paige, R. F., Penzenstadler, B., Seyff, N., Syriani, E., & Venters, C. C. (2020). Toward model-driven sustainability evaluation. Commun. ACM, 63(3), 80-91. https://doi.org/10.1145/3371906
• Perpetual Assurances for Self-Adaptive Systems
  
  Weyns, D., Bencomo, N., Calinescu, R., Cámara, J., Ghezzi, C., Grassi, V., Grunske, L., Inverardi, P., Jézéquel, J.-M., Malek, S., Mirandola, R., Mori, M., & Tamburrelli, G. (2019). Perpetual Assurances for Self-Adaptive Systems. CoRR, abs/1903.04771.
• Models@run.time: a guided tour of the state of the art and research challenges
  
  Bencomo, N., Götz, S., & Song, H. (2019). Models@run.time: a guided tour of the state of the art and research challenges. Softw. Syst. Model., 18(5), 3049-3082. https://doi.org/10.1007/s10270-018-00712-x
• The role of models@run.time in supporting on-the-fly interoperability
  
  Bencomo, N., Bennaceur, A., Grace, P., Blair, G., & Issarny, V. (2013). The role of models@run.time in supporting on-the-fly interoperability. Computing, 95(3), 167-190. https://doi.org/10.1007/s00607-012-0224-x

Report

• Responsible AI governance: A response to UN interim report on governing AI for humanity
  
  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., … Iniesta, R. (2024). Responsible AI governance: A response to UN interim report on governing AI for humanity. United Nations. https://doi.org/10.48550/arXiv.2412.12108