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| 2. |
- Ameller, D., et al.
(författare)
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Handling Non-functional Requirements in Model-Driven Development: An Ongoing Industrial Survey
- 2015
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Ingår i: 2015 IEEE 23rd International Requirements Engineering Conference (Re). - : IEEE. - 9781467369053
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Konferensbidrag (refereegranskat)abstract
- Model-Driven Development (MDD) is no longer a novel development paradigm. It has become mature from a research perspective and recent studies show its adoption in industry. Still, some issues remain a challenge. Among them, we are interested in the treatment of non-functional requirements (NFRs) in MDD processes. Very few MDD approaches have been reported to deal with NFRs (and they do it in a limited way). However, it is clear that NFRs need to be considered somehow in the final product of the MDD process. To better understand how NFRs are integrated into the existing MDD approaches, we have initiated the NFR4MDD project, a multi-national empirical study, based on interviews with companies working on MDD projects. Our project aims at surveying the state of the practice for this topic. In this paper, we summarize our research protocol and present the current status of our study. The discussion will focus on the peculiarities of our study's context and organization involving about 20 researchers from 8 European countries.
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| 3. |
- Daneva, M., et al.
(författare)
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Welcome from the workshop chairs
- 2014
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Ingår i: 2014 IEEE 4th International Workshop on Empirical Requirements Engineering, EmpiRE 2014 - Proceedings. - 9781479963379
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Oriol, M., et al.
(författare)
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FAME : Supporting continuous requirements elicitation by combining user feedback and monitoring
- 2018
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Ingår i: Proceedings - 2018 IEEE 26th International Requirements Engineering Conference, RE 2018. - : Institute of Electrical and Electronics Engineers Inc.. - 9781538674185 ; , s. 217-227
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Konferensbidrag (refereegranskat)abstract
- Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation. © 2018 IEEE.
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| 5. |
- Zavala, E., et al.
(författare)
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Adaptive monitoring for autonomous vehicles using the HAFLoop architecture
- 2021
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Ingår i: Enterprise Information Systems. - : Informa UK Limited. - 1751-7575 .- 1751-7583. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- Current Self-Adaptive Systems (SASs) such as Autonomous Vehicles (AVs) are systems able to deal with highly complex contexts. However, due to the use of static feedback loops they are not able to respond to unanticipated situations such as sensor faults. Previously, we have proposed HAFLoop (Highly Adaptive Feedback control Loop), an architecture for adaptive loops in SASs. In this paper, we incorporate HAFLoop into an AV solution that leverages machine learning techniques to determine the best monitoring strategy at runtime. We have evaluated our solution using real vehicles. Evaluation results are promising and demonstrate the great potential of our proposal.
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| 6. |
- Zavala, E., et al.
(författare)
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HAFLoop: An architecture for supporting Highly Adaptive Feedback Loops in self-adaptive systems
- 2020
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Ingår i: Future Generation Computer Systems-the International Journal of Escience. - : Elsevier BV. - 0167-739X. ; 105:April, s. 607-630
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Tidskriftsartikel (refereegranskat)abstract
- Most of the current self-adaptive systems (SASs) rely on static feedback loops such as the IBM's MAPEK loop for managing their adaptation process. Static loops do not allow SASs to react to runtime events such as changing adaptation requirements or MAPE-K elements' faults. In order to address this issue, some solutions have emerged for manually or automatically perform changes on SASs' feedback loops. However, from the software engineering perspective, most of the proposals cannot be reused or extended by other SASs. In this paper, we present HAFLoop (Highly Adaptive Feedback control Loop), a generic architectural proposal that aims at easing and fastening the design and implementation of adaptive feedback loops in modern SASs. Our solution enables both structural and parameter adaptation of the loop elements. Moreover, it provides a highly modular design that allows SASs' owners to support a variety of feedback loop settings from centralized to fully decentralized. In this work, HAFLoop has been implemented as a framework for Java-based systems and evaluated in two emerging software application domains: self-driving vehicles and IoT networks. Results demonstrate that our proposal easies and accelerates the development of adaptive feedback loops as well as how it could help to address some of the most relevant challenges of self-driving vehicles and IoT applications. Concretely, HAFLoop has demonstrated to improve SASs' feedback loops' runtime availability and operation. (C) 2019 Elsevier B.V. All rights reserved.
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| 7. |
- Zavala, E., et al.
(författare)
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SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime
- 2018
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Ingår i: Expert Systems with Applications. - : Elsevier BV. - 0957-4174. ; 98, s. 166-188
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Tidskriftsartikel (refereegranskat)abstract
- Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains. (C) 2018 Elsevier Ltd. All rights reserved.
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