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Outputs (51)

Latency-aware RDMSim: Enabling the Investigation of Latency in Self-Adaptation for the Case of Remote Data Mirroring (2024)
Conference Proceeding
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. . https://doi.org/10.1145/3643915.3644106

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.

Code Gradients: Towards Automated Traceability of LLM-Generated Code (2024)
Conference Proceeding
North, M., Atapour-Abarghouei, A., & Bencomo, N. (in press). Code Gradients: Towards Automated Traceability of LLM-Generated Code.

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.

Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements (2024)
Journal Article
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), 1-44. https://doi.org/10.1145/3643889

Approaches that support the decision-making of self-adaptive and autonomous systems (SAS) often consider an idealized situation where (i) the system’s state is treated as fully observable by the monitoring infrastructure, and (ii) adaptation actions... Read More about Decision Making for Self-adaptation based on Partially Observable Satisfaction of Non-Functional Requirements.

Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems (2024)
Conference Proceeding
Camara, J., Hahner, S., Perez-Palacin, D., Vallecillo, A., Acosta, M., Bencomo, N., …Gerasimou, S. (in press). Uncertainty Flow Diagrams: Towards a Systematic Representation of Uncertainty Propagation and Interaction in Adaptive Systems. In Proceedings 2024 IEEE/ACM 19th Symposium on Software Engineering for Adaptive and Self-Managing Systems

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.

Automated Provenance Collection at Runtime as a Cross-Cutting Concern (2023)
Conference Proceeding
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) (276-285). https://doi.org/10.1109/MODELS-C59198.2023.00057

Autonomous decision-making is increasingly applied to handle highly dynamic, uncertain environments: as incorrect decisions can cause serious harm to individuals or society, there is a need for accountability. For systems that use runtime models to r... Read More about Automated Provenance Collection at Runtime as a Cross-Cutting Concern.

To download or not to download the Covid-19 Track and Trace App? What is more influential in users’ minds? (2023)
Journal Article
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

Objectives to investigate the role of values in technology acceptance in general and in the context of the UK Covid Track and Trace App. Methods A survey and interview study was conducted to elicit users’ perceptions of values in general, values... Read More about To download or not to download the Covid-19 Track and Trace App? What is more influential in users’ minds?.

History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems (2022)
Conference Proceeding
Parra-Ullauri, J., Garcia-Dominguez, A., Bencomo, N., & Garcia Paucar, L. (2022). History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems. . https://doi.org/10.1145/3550356.3561538

The complexity of real-world problems requires modern software systems to autonomously adapt and modify their behaviour at run time to deal with internal and external challenges and contexts. Consequently, these self-adaptive systems (SAS) can show u... Read More about History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems.

The Implications of ‘Soft’ Requirements (2022)
Conference Proceeding
Sutcliffe, A., Sawyer, P., & Bencomo, N. (2022). The Implications of ‘Soft’ Requirements. In E. Knauss, G. Mussbacher, C. Arora, M. Bano, & J. Schneider (Eds.), 2022 IEEE 30th International Requirements Engineering Conference (RE) (178-188). https://doi.org/10.1109/re54965.2022.00022

A new focus for RE is investigated as ‘soft’ requirements which extends non-functional requirements / soft goals with a collection of people-oriented phenomena: values, motivations, emotions, and other socio-political issues that may influence the re... Read More about The Implications of ‘Soft’ Requirements.

The Uncertainty Interaction Problem in Self-Adaptive Systems (2022)
Journal Article
Camara, J., Troya1, J., Vallecillo, A., Bencomo, N., Calinescu, R., Cheng, B., …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

The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of them tend... Read More about The Uncertainty Interaction Problem in Self-Adaptive Systems.

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 (2022)
Conference Proceeding
Garcia-Paucar, L., Bencomo, N., Sutcliffe, A., & Sawyer, P. (2022). 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. . https://doi.org/10.1145/3477314.3507147

Soft requirements (such as human values, motivations, and personal attitudes) can strongly influence technology acceptance. As such, we need to understand, model and predict decisions made by end users regarding the adoption and utilization of softwa... Read More about 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.

Decision-Making under Uncertainty: Be Aware of your Priorities (2022)
Journal Article
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

Self-adaptive systems (SASs) are increasingly leveraging autonomy in their decision-making to manage uncertainty in their operating environments. A key problem with SASs is ensuring their requirements remain satisfied as they adapt. The trade-off ana... Read More about Decision-Making under Uncertainty: Be Aware of your Priorities.

The Secret to Better AI and Better Software (Is Requirements Engineering) (2021)
Journal Article
Bencomo, N., Guo, J., Harrison, R., Heyn, H., & 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

Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requiremen... Read More about The Secret to Better AI and Better Software (Is Requirements Engineering).

Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning (2021)
Journal Article
Parra-Ullauri, J. M., Garcıa-Domınguez, A., Bencomo, N., Zheng, C., Zhen, C., Boubeta-Puig, J., …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

Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exa... Read More about Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning.

Cronista: a multi-database automated provenance collection system for runtime models (2021)
Journal Article
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

Context: Decision making by software systems that face uncertainty needs tracing to support understandability, as accountability is crucial. While logging has been essential to support explanations and understandability of behaviour, several issues s... Read More about Cronista: a multi-database automated provenance collection system for runtime models.

Temporal Models for History-Aware Explainability (2020)
Conference Proceeding
Ullauri, J. M. P., García-Domínguez, A., Paucar, L. H. G., & Bencomo, N. (2020). Temporal Models for History-Aware Explainability. In A. Gherbi, W. Hamou-Lhadj, & A. Bali (Eds.), . https://doi.org/10.1145/3419804.3420276

On one hand, there has been a growing interest towards the application of AI-based learning and evolutionary programming for self-adaptation under uncertainty. On the other hand, self-explanation is one of the self-* properties that has been neglecte... Read More about Temporal Models for History-Aware Explainability.

RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (2019)
Conference Proceeding
Bencomo, N., & Paucar, L. H. G. (2019). RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning. In M. Kessentini, T. Yue, A. Pretschner, S. Voss, & L. Burgueño (Eds.), . https://doi.org/10.1109/models.2019.00005

[Context/Motivation] A model at runtime can be defined as an abstract representation of a system, including its structure and behaviour, which exist alongside with the running system. Runtime models provide support for decision-making and reasoning b... Read More about RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning.

Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks (2013)
Conference Proceeding
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.), . https://doi.org/10.1007/978-3-642-37422-7_16

[Context/Motivation] Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total ful... Read More about Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks.

Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances (2013)
Conference Proceeding
Lemos, R. D., Garlan, D., Ghezzi, C., Giese, H., Andersson, J., Litoiu, 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.), . https://doi.org/10.1007/978-3-319-74183-3_1

The important concern for modern software systems is to become more cost-effective, while being versatile, flexible, resilient, dependable, energy-efficient, customisable, configurable and self-optimising when reacting to run-time changes that may oc... Read More about Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances.

Dynamic decision networks for decision-making in self-adaptive systems: a case study (2013)
Conference Proceeding
Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Dynamic decision networks for decision-making in self-adaptive systems: a case study. In M. Litoiu, & J. Mylopoulos (Eds.), . https://doi.org/10.1109/seams.2013.6595498

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. Howe... Read More about Dynamic decision networks for decision-making in self-adaptive systems: a case study.