Øystein Haugen

• Nguyen, Hoa Thi; Olsson, Roland & Haugen, Øystein (2024). Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). ISSN 2644-1284. 5, p. 91–108. doi: 10.1109/OJIES.2024.3363500.
• Nguyen, Hoa Thi; Haugen, Øystein & Olsson, Roland (2023). Imitation Learning from Operator Experiences for a Real time CNC Machine Controller. IEEE Conference on Industrial Informatics. ISSN 1935-4576. doi: 10.1109/INDIN51400.2023.10218164. Full text in Research Archive
• Dinh, Nga & Haugen, Øystein (2022). Charging Scheduling Algorithm for Wireless-Powered Communication Networks, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-8025-3. doi: 10.1109/IECON49645.2022.9968357.
• Nguyen, Hoa Thi & Haugen, Øystein (2022). Building Experimental Laboratory for Digital Twin in Service Oriented Architecture, 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems - ICPS. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-9770-1. doi: 10.1109/ICPS51978.2022.9816926. Full text in Research Archive Show summary

  The concept of Digital Twins is still in an infant state of development. Digital Twins are often built as a tool to aid in better understanding of physical systems through simulation. They can be used to visualize information during operations and provide instructions during training or execution of procedures. The use of Digital Twins to test is appealing as it can be done quickly and safely. However, testing without inclusion of the physical system can lead to a reality gap. The reality gap can lead to high risks when applying concepts tested on digital Twins to the physical system directly. Sometimes interaction with the physical system is unfeasible. In this paper, we present an experimental laboratory that we built to provide a platform for the development of high quality Digital Twins through a feedback loop. The physical system is a Palfinger crane. Our replicate physical twin is a Universal collaborative industrial robot model UR16e due to its similar anatomy to the crane. The RoboDK simulation software was used to rapidly develop a digital twin of the UR16e. We demonstrate a solution to the interoperability problem in digital Twins using the monitoring adaptation loop from the Autonomic Adaptation System of the Arrowhead Framework.

• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2022). Bug Localization in Model-Based Systems in the Wild. ACM Transactions on Software Engineering and Methodology. ISSN 1049-331X. 31(1), p. 10:1–10:32. doi: 10.1145/3472616.
• Delsing, Jerker; Kulcsár, Géza & Haugen, Øystein (2022). SysML modeling of service-oriented system-of-systems. Innovations in Systems and Software Engineering. ISSN 1614-5046. doi: 10.1007/s11334-022-00455-5. Full text in Research Archive
• Lam, An Ngoc; Haugen, Øystein & Delsing, Jerker (2022). Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). ISSN 2644-1284. 3, p. 128–145. doi: 10.1109/OJIES.2022.3149093. Full text in Research Archive
• Lam, An Ngoc; Haugen, Øystein & Delsing, Jerker (2021). Interoperability for Industrial Internet of Things Based on Service-oriented Architecture, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-3554-3. doi: 10.1109/IECON48115.2021.9589102. Full text in Research Archive
• Dinh, Nga; Tveito, Øystein & Haugen, Øystein (2021). Performance Evaluation for Energy Harvesting (EH)-based Sensor Nodes Under LoRaWAN Connectivity. In Bahk, Saewoong; Tran-Gia, Phuoc; Van der Spiegel, Jan & Quynh, Nguyen Xuan (Ed.), 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-5471-8. p. 64–69. doi: 10.1109/ICCE48956.2021.9352044. Show summary

  Sustainable operation is one of the key challenges for Internet of Thing (IoT) sensor nodes because they are usually powered by limited battery power. Many IoT applications require the sensor nodes to operate reliably and consistently for a long period of time. Approaches to prolong the lifespan of sensor nodes include sleep-mode mechanism, harvesting energy from the environment (such as light, wind, water mills...) and from a dedicated power supply. Energy harvesting (EH) from nature has emerged as especially promising because it is clean, free and does not interrupt operation of the nodes. Long-Range Wide Area Network (LoRaWAN) can provide low-power, low-cost, and long-range communication for IoT nodes. In LoRaWAN, a node can extend its lifetime by using a deep-sleep mechanism. Even though the lifetime can be extended by the deep-sleep mechanism, it is expected to further extend the lifespan to reduce or eliminate the replacement of outage devices. Due to the potentials of LoRaWAN for IoT and the importance of EH approach to provide sustainable operation for IoT nodes, this paper focuses on using both EH and the deep-sleep mechanism in LoRaWAN to significantly prolong the lifetime of sensor nodes. In particular, the paper proposes a new power metric, called power gradient, to evaluate power performance of the network. The paper also estimates the performance of EH-based sensor nodes in terms of power gradient and lifetime. The proposed metric provides some insights on parameters configuration to achieve the desirable lifetime of EH-based nodes under LoRaWAN.

• Shahraki, Amin; Taherkordi, Amirhosein & Haugen, Øystein (2021). TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks. Computer Networks. ISSN 1389-1286. 194. doi: 10.1016/j.comnet.2021.108125. Full text in Research Archive
• Shimbara, Daisuke; Saeki, Motoshi; Hayashi, Shinpei & Haugen, Øystein (2021). Handling Quantity in Variability Models for System-of-Systems. International journal of software engineering and knowledge engineering. ISSN 0218-1940. 31(5), p. 693–724. doi: 10.1142/S0218194021500200.
• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2021). Handling nonconforming individuals in search-based model-driven engineering: nine generic strategies for feature location in the modeling space of the meta-object facility. Software and Systems Modeling (SoSyM). ISSN 1619-1366. 20(5), p. 1653–1688. doi: 10.1007/s10270-021-00870-5.
• Dinh, Nga & Haugen, Øystein (2020). Analysis and Power Scheduling Algorithm in Wireless Powered Communication Networks, 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-9722-7. doi: 10.1109/WiMob50308.2020.9253379.
• Garcia Ceja, Enrique Alejandro; Hugo, Åsmund Pedersen; Morin, Brice; Hansen, Per Olav; Martinsen, Espen & Lam, An Ngoc [Show all 7 contributors for this article] (2020). A Feature Importance Analysis for Soft-Sensing-Based Predictions in a Chemical Sulphonation Process, Proceedings of 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-6389-5. p. 62–66. doi: 10.1109/ICPS48405.2020.9274769. Show summary

  In this paper we present the results of a feature importance analysis of a chemical sulphonation process. The task consists of predicting the neutralization number (NT), which is a metric that characterizes the product quality of active detergents. The prediction is based on a dataset of environmental measurements, sampled from an industrial chemical process. We used a soft-sensing approach, that is, predicting a variable of interest based on other process variables, instead of directly sensing the variable of interest. Reasons for doing so range from expensive sensory hardware to harsh environments, e.g., inside a chemical reactor. The aim of this study was to explore and detect which variables are the most relevant for predicting product quality, and to what degree of precision. We trained regression models based on linear regression, regression tree and random forest. A random forest model was used to rank the predictor variables by importance. Then, we trained the models in a forward-selection style by adding one feature at a time, starting with the most important one. Our results show that it is sufficient to use the top 3 important variables, out of the 8 variables, to achieve satisfactory prediction results. On the other hand, Random Forest obtained the best result when trained with all variables.

• Shahraki, Amin; Taherkordi, Amirhosein; Haugen, Øystein & Eliassen, Frank (2020). A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms. IEEE Transactions on Network and Service Management. ISSN 1932-4537. doi: 10.1109/TNSM.2020.3035315. Full text in Research Archive
• Shahraki, Amin; Taherkordi, Amirhosein; Haugen, Øystein & Eliassen, Frank (2020). Clustering objectives in wireless sensor networks: A survey and research direction analysis. Computer Networks. ISSN 1389-1286. 180. doi: 10.1016/j.comnet.2020.107376. Full text in Research Archive
• Shahraki, Amin; Abbasi, Mahmoud & Haugen, Øystein (2020). Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 94. doi: 10.1016/j.engappai.2020.103770.
• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2020). A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. 31(12), p. 1–28. doi: 10.1002/ett.3924. Full text in Research Archive
• Lam, An Ngoc & Haugen, Øystein (2019). Implementing OPC-UA services for Industrial Cyber-Physical Systems in Service-Oriented Architecture, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE conference proceedings. ISSN 978-1-7281-4878-6. p. 5486–5492. doi: 10.1109/IECON.2019.8926972.
• Lam, An Ngoc & Haugen, Øystein (2019). Applying semantics into Service-oriented IoT Framework, The 17th IEEE International Conference on Industrial Informatics (IEEE-INDIN 2019): Proceedings. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781-7281-2927-3. p. 206–213. doi: 10.1109/INDIN41052.2019.8972295. Show summary

  Introducing semantics into the Internet of Things (IoT) has been attracting increasing attention from researchers and industrial practitioners. Semantic technologies have been used to enable interoperability as well as deal with the heterogeneity, massive scale, and dynamic nature of IoT resources. With the emergence of Industry 4.0, semantic technologies arise as a potential approach toward information modeling and dynamic reconfiguration of highly complex automation systems with high diversity of domains, protocols, tools or hardware platforms. Applying semantics into existing IoT frameworks requires a thorough understanding of the framework architectures as well as careful considerations of different semantic technologies. To support this process, we survey the literature on the contributions and usage of semantics in IoT. We find that semantics are mainly used to handle interoperable systems and heterogeneous standards. In this paper, we also propose procedures for applying semantics into IoT frameworks. Further, we present our idea of using semantics to enable dynamical orchestration of services within the Arrowhead Framework - an IoT framework that supports the development of industrial automation systems.

• Shahraki, Amin & Haugen, Øystein (2019). An outlier detection method to improve gathered datasets for network behavior analysis in IoT. Journal of Communications. ISSN 1796-2021. 14(6), p. 455–462. doi: 10.12720/jcm.14.6.455-462. Full text in Research Archive
• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost. In Perković, Toni (Eds.), 2019 4thInternational Conference on Smart and Sustainable Technologies(SpliTech). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-953-290-091-0. doi: 10.23919/SpliTech.2019.8783016.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2019). An approach for bug localization in models using two levels: model and metamodel. Software and Systems Modeling (SoSyM). ISSN 1619-1366. 18(6), p. 3551–3576. doi: 10.1007/s10270-019-00727-y. Show summary

  Bug localization is a common task in software engineering, especially when maintaining and evolving software products. This paper introduces a bug localization approach that, in contrast to existing source code approaches, takes advantage of domain information found in the model and the metamodel. Throughout this paper, we present an approach for bug localization in models (BLiM2) that applies the source code ideas for bug localization (textual similarity to the bug description and the Defect Localization Principle) and takes advantage of the domain information from the model and the metamodel. We evaluated our approach in BSH, a real-world industrial case study in the induction hob domain measuring the results in terms of recall, precision, the combination of both the F-measure and the Matthews correlation coefficient. Our study shows that our BLiM2 approach, which combines information from the model and the metamodel for the textual similarity and differentiates between the timespan from the model and metamodel, provides the best results in this work. We also performed a statistical analysis to provide evidence of the significance of the results. The values obtained show that there exist significant differences in the performance of the best BLiM2 approach with the approach used by our industrial partner. Finally, the effect size statistics reveals that the best BLiM2 approach obtains better results in the 78% of the times in the worst case.

• Lam, An Ngoc & Haugen, Øystein (2018). Supporting IoT semantic interoperability with autonomic computing, Proceeding of the 1st IEEE international conference on industrial cyber-physical systems (ICPS 2018). . IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538665305. p. 761–767. doi: 10.1109/ICPHYS.2018.8390803.
• Barner, Simon; Chauvel, Franck; Diewald, Alexander; Eizaguirre, Fernando; Haugen, Øystein & Migge, Jörn [Show all 7 contributors for this article] (2018). Modeling and Development Process. In Ahmadian, Hamidreza; Obermaisser, Roman & Pérez, Jon (Ed.), Distributed Real-Time Architecture for Mixed-Criticality Systems. CRC Press. ISSN 9780815360643.
• Migge, Jörn; Balbastre, Patricia; Barner, Simon; Chauvel, Franck; Craciunas, Silviu S. & Diewald, Alexander [Show all 12 contributors for this article] (2018). Algorithms and Tools. In Ahmadian, Hamidreza; Obermaisser, Roman & Pérez, Jon (Ed.), Distributed Real-Time Architecture for Mixed-Criticality Systems. CRC Press. ISSN 9780815360643.
• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2017). Achieving Feature Location in Families of Models Through the Use of Search-Based Software Engineering. IEEE Transactions on Evolutionary Computation. ISSN 1089-778X. 22(3), p. 363–377. doi: 10.1109/TEVC.2017.2751100.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2017). On the influence of models at run-time traces in dynamic feature location. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10376 LNCS, p. 90–105. doi: 10.1007/978-3-319-61482-3_6.
• Reinhartz-Berger, Iris; Figl, Kathrin & Haugen, Øystein (2017). Investigating styles in variability modeling: Hierarchical vs. constrained styles. Information and Software Technology. ISSN 0950-5849. 87, p. 81–102. doi: 10.1016/j.infsof.2017.01.012.
• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2017). Leveraging variability modeling to address metamodel revisions in Model-based Software Product Lines. Computer Languages, Systems & Structures. ISSN 1477-8424. 48, p. 20–38. doi: 10.1016/j.cl.2016.08.003.
• Vasilevskiy, Anatoly; Morin, Brice; Haugen, Øystein & Evensen, Pål (2016). Agile development of home automation system with ThingML. IEEE Conference on Industrial Informatics. ISSN 1935-4576. p. 337–344. doi: 10.1109/INDIN.2016.7819183. Show summary

  Advances in the Internet of Things (IoT) domain thrusts home automation into the spotlight. Home automation is a heterogeneous realm with various and often not compatible technologies. As any not standardized area, this realm is always disrupted by new, emerging technologies and standards. Home is a dynamic environment which constantly grows and evolves. To keep up with changes, home automation algorithms are tweaked and modified over time. To tackle diversity, we employ model driven approaches to weave different technologies and facilitate home automation tasks. We show how modelling techniques improve portability, scalability and simulation of the home automation features. We present two use-cases developed in cooperation with industry partners, report our experience in applying ThingML and the Arrowhead framework in the home automation domain.

• Lam, An Ngoc & Haugen, Øystein (2016). Complex event processing in ThingML. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9959, p. 20–35. doi: 10.1007/978-3-319-46613-2_2.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2016). An infrastructure for generating run-time model traces for maintenance tasks. CEUR Workshop Proceedings. ISSN 1613-0073. 1742, p. 35–42.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2016). Feature location through the combination of run-time architecture models and information retrieval. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9959 LNCS, p. 180–195. doi: 10.1007/978-3-319-46613-2_12.
• Vasilevskiy, Anatoly; Morin, Brice; Haugen, Øystein & Evensen, Pål (2016). Agile Development of Home Automation System with ThingML, 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-2870-2. p. 337–344. doi: 10.1109/INDIN.2016.7819183. Show summary

  Advances in the Internet of Things (IoT) domain thrusts home automation into the spotlight. Home automation is a heterogeneous realm with various and often not compatible technologies. As any not standardized area, this realm is always disrupted by new, emerging technologies and standards. Home is a dynamic environment which constantly grows and evolves. To keep up with changes, home automation algorithms are tweaked and modified over time. To tackle diversity, we employ model driven approaches to weave different technologies and facilitate home automation tasks. We show how modelling techniques improve portability, scalability and simulation of the home automation features. We present two use-cases developed in cooperation with industry partners, report our experience in applying ThingML and the Arrowhead framework in the home automation domain.

• Vasilevskiy, Anatoly; Chauvel, Franck & Haugen, Øystein (2016). Toward Robust Product Realisation in Software Product Lines, SPLC '16 Proceedings of the 20th International Systems and Software Product Line Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-4050-2. p. 184–193. doi: 10.1145/2934466.2934484. Show summary

  Product derivation is a building process of products from selected features in software product lines (SPLs). Realisation paves the way for automatic product derivation. A realisation defines a mapping between abstract features in a feature tree and their implementation artefacts in a model, and therefore governs the derivation of a new product. We experience that a realisation is not always straightforward and robust against modifications in the model. In the paper, we introduce an approach to build robust realisations. It consists of automated planning techniques and a layered architecture to yield a product. We demonstrate how our approach can leverage modern means of software design, development and validation. We evaluate the approach on a use-case provided by an industry partner and compare our technique to the existing realisation layer in the Base Variability Resolution (BVR) language.

• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2016). Feature location in model-based software product lines through a genetic algorithm. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9679, p. 39–54. doi: 10.1007/978-3-319-35122-3_3.
• Vasilevskiy, Anatoly; Haugen, Øystein; Chauvel, Franck; Johansen, Martin Fagereng & Shimbara, Daisuke (2015). The BVR tool bundle to support product line engineering, SPLC '15 Proceedings of the 19th International Conference on Software Product Line, July 20-24, Nashville, TN. Association for Computing Machinery (ACM). ISSN 978-1-4503-3613-0. p. 380–384. doi: 10.1145/2791060.2791094. Show summary

  The Base Variability Resolution (BVR) is a modern language to build software product lines (SPL). The language incorporates advanced concepts for feature modeling, reuse and realization of components in SPL. The BVR bundle implements and supports the language. The tool covers design, implementation and quality assurance to close the development cycle. The bundle enables feature modeling, resolution, realization and derivation of products, their testing and analysis. We integrate the SPLCA additions to provide the state of the art algorithms for analysis. The project is open-source and available for practitioners. The tool consists of Eclipse plug-ins which work seamlessly together as well as separate stand-alone components. We describe how the tool collaboration contributes to variability modeling. In addition, we present how the bundle applies well-known design patterns, principals to achieve synergy between components.

• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2015). Leveraging Models at Run-Time to Retrieve Information for Feature Location. CEUR Workshop Proceedings. ISSN 1613-0073. 1474, p. 51–60.
• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2015). Addressing metamodel revisions in model-based software product lines. In Kästner, Christian & Gokhālé , Anirüddhā (Ed.), Proceedings of the 2015 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences. Association for Computing Machinery (ACM). ISSN 978-1-4503-3687-1. p. 161–170. doi: 10.1145/2814204.2814214. Show summary

  Metamodels evolve over time, which can break the conformance between the models and the metamodel. Model migration strategies aim to co-evolve models and metamodels together, but their application is not fully automatizable and is thus cumbersome and error prone. We introduce the Variable MetaModel (VMM) strategy to address the evolution of the reusable model assets of a model-based Software Product Line. The VMM strategy applies variability modeling ideas to express the evolution of the metamodel in terms of commonalities and variabilities. When the metamodel evolves, the models continue to conform to the VMM, avoiding the need for migration. We have applied both the traditional migration strategy and the VMM strategy to a retrospective case study that includes 13 years of evolution of our industrial partner, an induction hobs manufacturer. The comparison between the two strategies shows better results for the VMM strategy in terms of model indirection, automation, and trust leak.

• Font, Jaime; Ballarin, Manuel; Haugen, Øystein & Cetina, Carlos (2015). Automating the variability formalization of a model family by means of common variability language, SPLC '15 Proceedings of the 19th International Conference on Software Product Line, July 20-24, Nashville, TN. Association for Computing Machinery (ACM). ISSN 978-1-4503-3613-0. p. 411–418. doi: 10.1145/2791060.2793678. Show summary

  The aim of domain engineering process is to define and realise the commonality and variability of a Software Product Line. In the context of a family of models, spotting the commonalities and differences may become cumbersome and error prone as the number of models and its complexity increases. This work presents an approach to automate the formalization of variability in a given family of models. As output, the variability is made explicit in terms of Common Variability Language. The model commonalities and differences are specified as placements over a base model and replacements in a model library. The resulting Software Product Line (SPL) enables the derivation of new product models by reusing the extracted model fragments. Furthermore, the SPL can be evolved by the creation of new models, which are in turn automatically decomposed as model fragments of the SPL. The approach has been validated with our industrial partner (BSH), an induction hobs company. Finally, we present five different evolution scenarios encountered during the validation.

• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2015). Building software product lines from conceptualized model patterns, SPLC '15 Proceedings of the 19th International Conference on Software Product Line, July 20-24, Nashville, TN. Association for Computing Machinery (ACM). ISSN 978-1-4503-3613-0. p. 46–55. doi: 10.1145/2791060.2791085. Show summary

  Software Product Lines (SPLs) can be established from a set of similar models. Establishing the Product Line by mechanically finding model differences may not be the best approach. The identified model fragments may not be seen as recognizable units by the application engineers. We propose to identify model patterns by human-in-the-loop and conceptualize them as reusable model fragments. The approach provides the means to identify and extract those model patterns and further apply them to existing product models. Model fragments obtained by applying our approach seem to perform better than mechanically found ones. It turns out that the repetition of a fragment does not guarantee its relevance as reusable asset for the SPL engineers and vice versa, a fragment that has not been repeated yet, may be relevant as a reusable asset. We have validated these ideas with our industrial partner BSH, an induction hobs manufacturer that generates the firmware of their products from a model-driven SPL.

• Shimbara, Daisuke & Haugen, Øystein (2015). Generating Configurations for System Testing with Common Variability Language. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9369, p. 221–237. doi: 10.1007/978-3-319-24912-4_16. Show summary

  Modern systems are composed of many subsystems, so it is necessary to understand how to combine them into complete functional systems. When testing a system that includes hardware, it is important that each selected test configuration delivers maximum information for covering many test cases. We have developed a method and a tool for creating a small set of effective test configurations that is based on a systematic approach to describing and formalizing the functionality of the whole system as well as its component into subsystems using feature models and relational notations between them. We applied our approach to an example point-of-sale checkout system consisting of one server and multiple registers.

• Berger, Thorsten; Stănciulescu, Ştefan; Øgård, Ommund; Haugen, Øystein; Larsen, Bo & Wąsowski, Andrzej (2014). To connect or not to connect: experiences from modeling topological variability. In Gnesi, Stefania; Fantechi, Alessandro; Heymans, Patrick; Rubin, Julia; Czarnecki, Krzysztof & Dhungana, Deepak (Ed.), SPLC '14 18th International Software Product Line Conference, Florence, Italy — September 15 - 19, 2014 . Association for Computing Machinery (ACM). ISSN 978-1-4503-2740-4. p. 330–339. doi: 10.1145/2648511.2648549. Show summary

  Variability management aims at taming variability in large and complex software product lines. To efficiently manage variability, it has to be modeled using formal representations, such as feature or decision models. Such models are efficient in many domains, where variability is about switching on and off features, or using parameters to customize products of the product line. However, variability can be represented in the form of a topology in domains where variability is about connecting components in a certain order, in specific interconnected hierarchies, or in different quantities. In this experience report, we explore topological variability within a case study of large-scale fire alarm systems. We identify core characteristics of the variability, derive modeling requirements, model the variability using UML2 class diagrams, and discuss the applicability of further variability modeling languages. We show that, although challenging, class diagrams can suffice to represent topological variability in order to generate a configurator tool. In contrast, modeling parallel and recursive structures, cycles, informal constraints, and orthogonal hierarchies were among the main experienced challenges that require further research.

• Reinhartz-Berger, Iris; Figl, Kathrin & Haugen, Øystein (2014). Comprehending Feature Models Expressed in CVL. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 8767, p. 501–517. doi: 10.1007/978-3-319-11653-2_31. Full text in Research Archive Show summary

  Feature modeling is a common way to present and manage variability of software and systems. As a prerequisite for effective variability management is comprehensible representation, the main aim of this paper is to investigate difficulties in understanding feature models. In particular, we focus on the comprehensibility of feature models as expressed in Common Variability Language (CVL), which was recommended for adoption as a standard by the Architectural Board of the Object Management Group. Using an experimental approach with participants familiar and unfamiliar with feature modeling, we analyzed comprehensibility in terms of comprehension score, time spent to complete tasks, and perceived difficulty of different feature modeling constructs. The results showed that familiarity with feature modeling did not influence the comprehension of mandatory, optional, and alternative features, although unfamiliar modelers perceived these elements more difficult than familiar modelers. OR relations were perceived as difficult regardless of the familiarity level, while constraints were significantly better understood by familiar modelers. The time spent to complete tasks was higher for familiar modelers.

• Vasilevskiy, Anatoly & Haugen, Øystein (2014). Resolution of Interfering Product Fragments in Software Product Line Engineering. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 8767, p. 467–483. doi: 10.1007/978-3-319-11653-2_29. Full text in Research Archive Show summary

  The Common Variability Language (CVL) allows deriving new products in a software product line by substituting fragments (placement) in the base model. Relations between elements of different placement fragments are an issue. Substitutions involving interfering placements may give unexpected and unintended results. However, there is a pragmatic need to define and execute fragments with interference. The need emerges when several diagrams are views of a single model, such as a placement in one diagram and a placement in another diagram reference the same model elements. We handle the issue by 1) classifying interfering fragments, 2) finding criteria to detect them, and 3) suggesting solutions via transformations. We implement our findings in the tooling available for downloading.

• Haugen, Øystein & Øgård, Ommund (2014). BVR - Better Variability Results. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 8769, p. 1–15. doi: 10.1007/978-3-319-11743-0_1. Full text in Research Archive Show summary

  We present BVR (Base Variability Resolution models), a language developed to fulfill the industrial needs in the safety domain for variability modeling. We show how the industrial needs are in fact quite general and that general mechanisms can be used to satisfy them. BVR is built on the OMG Revised Submission of CVL (Common Variability Language), but is simplified and enhanced relative to that language.

• Wendland, Marc-Florian; Schneider, Martin & Haugen, Øystein (2013). Evolution of the UML Interactions Metamodel. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 8107, p. 405–421. doi: 10.1007/978-3-642-41533-3_25. Full text in Research Archive Show summary

  UML Interactions represent one of the three different behavior kinds of the UML. In general, they specify the exchange of messages among parts of a system. Although UML Interactions can reside on different level of abstractions, they seem to be sufficiently elaborated for a higher-level of abstraction where they are used for sketching the communication among parts. Its metamodel reveals some fuzziness and imprecision where definitions should be accurate and concise, though. In this paper, we propose improvements to the UML Interactions’ metamodel for Message arguments and Loop CombinedFragments that make them more versatile. We will justify the needs for the improvements by precisely showing the shortcomings of the related parts of the metamodel. We demonstrate the expressiveness of the improvements by applying them to examples that current Interactions definition handles awkwardly

• Haugen, Øystein (2013). Domain-Specific Languages and Standardization: Friends or Foes? In Reinhartz-Berger, Iris; Sturm, Arnon; Clark, Tony; Cohen, Sholom & Bettin, Jorn (Ed.), Domain Engineering. Product Lines, Languages, and Conceptual Models. Springer. ISSN 978-3-642-36653-6. p. 159–186. doi: 10.1007/978-3-642-36654-3_7. Show summary

  Domain specific languages capture the domain knowledge through the constructs of the language, but making a good language takes more than combining a set of domain concepts in some random fashion. Creating a good language requires knowledge not only from the domain but also from the domain of language design. Generic abstraction concepts turn out to be useful for many different domains and thus for domain specific languages. In this chapter we discuss how domain specific languages can benefit from standardized generic languages to cope with abstraction needs. A successful combination will keep the domain specific language simple and its implementation maintainable while the generic language will add expressiveness and structuring means. We give examples of domain specific languages as well as general ones and use the examples to illus-trate our advice on how to make a good language. We share experiences of lan-guage evolution and finally show an example of combining a generic language for variability with a domain specific language for train signaling.

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• Wąsowski, Andrzej; Paige, Richard & Haugen, Øystein (2018). MODELS '18 Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, Copenhagen, Denmark — October 14 - 19, 2018 . Association for Computing Machinery (ACM). ISBN 978-1-4503-4949-9. 478 p.
• Haugen, Øystein; Reed, Rick & Gotzhein, Reinhard (2013). System Analysis and Modeling: Theory and Practice. Springer. ISBN 978-3-642-36756-4. 248 p.

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• Nguyen, Hoa Thi; Haugen, Øystein & Olsson, Roland (2023). Imitation Learning from Operator Experiences for a Real time CNC Machine Controller.
• Haugen, Øystein (2022). Requirements and state of the art.
• Haugen, Øystein (2022). Tingenes Internett – en vidunderlig ny verden?
• Haugen, Øystein (2022). Practitioners’ Validation of systems.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2022). Feature Location in Models (FLiM): Design Time and Runtime. In Lopez-Herrejon, Roberto Erick; Martinez, Jabier; Klewerton Guez Assunção, Wesley; Ziadi, Tewfik; Acher, Mathieu & Vergilio, Silvia (Ed.), Handbook of Re-Engineering Software Intensive Systems into Software Product Lines. Springer. ISSN 978-3-031-11685-8. p. 79–113. doi: 10.1007/978-3-031-11686-5_4.
• Dinh, Thi Thuy Nga; Tveito, Øystein & Haugen, Øystein (2021). Performance Evaluation for Energy Harvesting (EH)-based Sensor Nodes Under LoRaWAN Connectivity.
• Dinh, Nga & Haugen, Øystein (2020). Analysis and Power Scheduling Algorithm in Wireless Powered Communication Networks.
• Haugen, Øystein & Stølen, Ketil (2020). Industri 4.0 – Hva kan vi lære av europeisk forskning? [Internet]. Webinar.
• Haugen, Øystein (2020). Tingenes internett for Industri4.0 med sikkerhet og fleksibilitet.
• Haugen, Øystein (2020). Digitalisering i Europa: erfaring og demo.
• Haugen, Øystein (2020). Productive4.0 - Opening the gates to the digital future .
• Haugen, Øystein (2020). Presentation of WP6 Standardization (part).
• Haugen, Øystein (2020). Standardizing SysML v2.
• Haugen, Øystein (2020). Productive4.0.
• Haugen, Øystein (2020). Standardization – promises and perils.
• Haugen, Øystein (2020). Productive 4.0 EU project update and potential standardization.
• Wąsowski, Andrzej; Paige, Richard F. & Haugen, Øystein (2020). Guest editorial to the special section on MODELS 2018. Software and Systems Modeling (SoSyM). ISSN 1619-1366. 19, p. 825–826. doi: 10.1007/s10270-020-00800-x.
• Haugen, Øystein & Rabi, Maben (2019). Internet of Things.
• Haugen, Øystein (2019). ECSEL-JU Lighthouse Projects Productive4.0 and Arrowhead Tools.
• Garcia-Ceja, Enrique; Hugo, Åsmund Pedersen; Morin, Brice; Hansen, Per Olav; Martinsen, Espen & Lam, An Ngoc [Show all 7 contributors for this article] (2019). Towards the Automation of a Chemical Sulphonation Process with Machine Learning.
• Shahraki, Amin; Geitle, Marius & Haugen, Øystein (2019). A distributed Fog node assessment model by using Fuzzy rules learned by XGBoost.
• Shahraki, Amin; Khojasteh Kaffash, Delaram & Haugen, Øystein (2018). A Review on the effects of IoT and Smart Cities Technologies on Urbanism.
• Shahraki, Amin & Haugen, Øystein (2018). Social ethics in Internet of Things:An outline and review.
• Haugen, Øystein (2018). Déjà vu – some personal bridges from the past to the future.
• Haugen, Øystein (2018). Modeling to reduce waste in chemical production.
• Haugen, Øystein (2018). Internet of Things.
• Haugen, Øystein (2018). Hva trenger en smartere industri?
• Haugen, Øystein (2018). Internet of Things.
• Haugen, Øystein (2018). Master Class on Variability Modeling: Basic Concepts of Product Line Engineering.
• Martinez, Imanol; Bouwer, Gebhard; Chauvel, Franck; Haugen, Øystein; Heinen, Robert & Klaes, Gernot [Show all 12 contributors for this article] (2018). Safety Certification of Mixed-Criticality Systems. In Ahmadian, Hamidreza; Obermaisser, Roman & Pérez, Jon (Ed.), Distributed Real-Time Architecture for Mixed-Criticality Systems. CRC Press. ISSN 9780815360643.
• Haugen, Øystein (2017). Internet of Things.
• Haugen, Øystein (2017). Internet of Things.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2017). On the Influence of Modification Timespan Weightings in the Location of Bugs in Models.
• Shahraki, Amin; Taherzadeh, Hamed & Haugen, Øystein (2017). Last Significant Trend Change Detection Method for Offline Poisson Distribution Datasets. Show summary

  Trend change detection methods find trends in a dataset. Datasets based on Poisson distribution are important to analyze since they mimic many different applications such as computer networks. Our use-cases are simulations of computer networks. The last significant trend is the last predominant trend in a time-series dataset. Our method is a matrix based trend change detection that can analyze datasets with variable sizes. Reducing the time complexity and increasing the accuracy when determining the last significant trend are the goals of our method. We compare our method with RuLSIF, a basic change point detection method, to illustrate the benefits of our approach.

• Font, Jaime; Arcega, Lorena; Haugen, Øystein & Cetina, Carlos (2016). Feature location in models through a genetic algorithm driven by information retrieval techniques.
• Arcega, Lorena; Font, Jaime; Haugen, Øystein & Cetina, Carlos (2016). Achieving Knowledge Evolution in Dynamic Software Product Lines.
• Haugen, Øystein; Vasilevskiy, Anatoly & Ogaard, Ommund (2015). Autronica Use Case: BVR Tool Bundle to Configure File Alarm System.
• Vasilevskiy, Anatoly; Haugen, Øystein; Chauvel, Franck; Johansen, Martin Fagereng & Shimbara, Daisuke (2015). The BVR Tool Bundle to Support Product Line Engineering. Show summary

  The Base Variability Resolution (BVR) is a modern language to build software product lines (SPL). The language incorporates advanced concepts for feature modeling, reuse and realization of components in SPL. The BVR bundle implements and supports the language. The tool covers design, implementation and quality assurance to close the development cycle. The bundle enables feature modeling, resolution, realization and derivation of products, their testing and analysis. We integrate the SPLCA additions to provide the state of the art algorithms for analysis. The project is open-source and available for practitioners. The tool consists of Eclipse plug-ins which work seamlessly together as well as separate stand-alone components. We describe how the tool collaboration contributes to variability modeling. In addition, we present how the bundle applies well-known design patterns, principals to achieve synergy between components.

• Berger, Thorsten; Stănciulescu, Stefan; Øgård, Ommund; Haugen, Øystein; Larsen, Bo & Wasowski, Andrzej (2014). To connect or not to connect: experiences from modeling topological variability. Show summary

  Variability management aims at taming variability in large and complex software product lines. To efficiently manage variability, it has to be modeled using formal representations, such as feature or decision models. Such models are efficient in many domains, where variability is about switching on and off features, or using parameters to customize products of the product line. However, variability can be represented in the form of a topology in domains where variability is about connecting components in a certain order, in specific interconnected hierarchies, or in different quantities. In this experience report, we explore topological variability within a case study of large-scale fire alarm systems. We identify core characteristics of the variability, derive modeling requirements, model the variability using UML2 class diagrams, and discuss the applicability of further variability modeling languages. We show that, although challenging, class diagrams can suffice to represent topological variability in order to generate a configurator tool. In contrast, modeling parallel and recursive structures, cycles, informal constraints, and orthogonal hierarchies were among the main experienced challenges that require further research

• Haugen, Øystein (2014). Comprehensibility of Feature Models: Experimenting with CVL.
• Haugen, Øystein (2014). BVR - Better Variability Results.
• Haugen, Øystein (2014). Standardizing a variability language. Show summary

  The Common Variability Language (CVL) is the name of a language which has been undergoing standardization within OMG, but now it has stalled. This talk will tell about CVL and the values of standardization in the area of product lines. The talk will also present some challenges and reasons why the standardization has stalled after having gone through all the technical committees of OMG.

• Haugen, Øystein (2014). The language consequences of exploring an industrial case using CVL. Show summary

  SINTEF and ITU have been involved with Autronica in the VARIES project. Autronica makes advanced fire alarm systems for diverse settings sucha as airports and cruise ships. We report from the experiences that we have gained from exploring the use of CVL (Common Variability Language) in this context and what consequences our experiences could have creating an enhancement of CVL also considering the fact that CVL standardization has been stalled.

• Haugen, Øystein; Wasowski, Andrzej & Czarnecki, Krzysztof (2013). CVL - Common Variability Language. Show summary

  The Common Variability Language (CVL) is a domain-independent language for specifying and resolving variability. It facilitates the specification and resolution of variability over any instance of any language defined using a MOF-based meta-model. This tutorial will present the results of work done by the Joint Submission Team against the OMG Request For Proposals on CVL. Prototype tooling for CVL will be demonstrated and made available for hands-on use by the participants

• Haugen, Øystein (2013). CVL – Common Variability Language or Chaos, Vanity and Limitations? Show summary

  What is CVL? History Language intro Chaos on standardization and its benefits and challenges Vanity Why are we doing this? why us? why do we think we can make this happen? Limitations standardization is not only technicalities The Future of CVL

• Haugen, Øystein (2013). CVL in a nutshell.
• Haugen, Øystein (2013). CVL - Common Variability Language or Chaos, Vanity and Limitations? Show summary

  What is CVL - the standardization initiative on variability modeling in OMG? As initiator and organizer of this initiative Ø.Haugen will talk about what CVL is, what the language status is, and what will happen next. He will argue why CVL will be fruitful rather than adding to everybody´s frustration and confusion. Central to this discussion is the role of standardization in achieving common understanding and tool interoperability. The talk will discuss how researchers can contribute to progress as well as impact in this field. Problems and challenges will be highlighted and hopefully discussions initiated. Information about CVL can be found at http://variabilitymodeling.org where e.g. the CVL Revised Submission having been submitted for OMG standardization is available.

• Font, Jaime; Haugen, Øystein & Cetina, Carlos (2017). Location of Features as Model Fragments and their Co-Evolution. Unipub forlag. ISSN 1501-7710.
• Zhang, Xiaorui; Haugen, Øystein & Møller-Pedersen, Birger (2013). Semantic Differencing for Product Line Evolution. SINTEF. ISSN 9788214053326. Show summary

  In model-driven product line development, the developer does not only specify high-level features, but also define how these features can be realized in terms of model editing operations. As a product line evolves over time, it is vital for the developer to have a precise overview of the added/removed products in the evolved product line, which is considered the semantic difference between the original and the evolved product line. However, current semantic differencing techniques for feature models only compare high-level features of the two product lines without considering their feature realizations. We address this challenge by proposing a semantic differencing approach which considers both the feature specification and realization of the original and the evolved product line. We illustrate our approach on the evolution of Common Variability Language (CVL)-based product lines. In particular, we define two semantic differencing operators for comparing the feature specification and the feature realization of two product lines, resulting in a set of "diff models". These two operators are implemented in Alloy and evaluated through a case study on train control product lines.

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