Malmö University Publications
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  • 1.
    Dzhusupova, Rimma
    et al.
    Eindhoven Univ Technol, Math & Comp Sci, Eindhoven, Netherlands..
    Bosch, Jan
    Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Choosing the right path for AI integration in engineering companies: A strategic guide2024In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 210, article id 111945Article in journal (Refereed)
    Abstract [en]

    The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

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  • 2.
    Eklund, Ulrik
    et al.
    Malmö högskola, School of Technology (TS).
    Bosch, Jan
    Architecture for embedded open software ecosystems2014In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 92, p. 128-142Article in journal (Refereed)
    Abstract [en]

    Software is prevalent in embedded products and may be critical for the success of the products, but manufacturers may view software as a necessary evil rather than as a key strategic opportunity and business differentiator. One of the reasons for this can be extensive supplier and subcontractor relationships and the cost, effort or unpredictability of the deliverables from the subcontractors are experienced as a major problem. The paper proposes open software ecosystem as an alternative approach to develop software for embedded systems, and elaborates on the necessary quality attributes of an embedded platform underlying such an ecosystem. The paper then defines a reference architecture consisting of 17 key decisions together with four architectural patterns, and provides the rationale why they are essential for an open software ecosystem platform for embedded systems in general and automotive systems in particular. The reference architecture is validated through a prototypical platform implementation in an industrial setting, providing a deeper understanding of how the architecture could be realised in the automotive domain. Four potential existing platforms, all targeted at the embedded domain (Android, OKL4, AUTOSAR and Robocop), are evaluated against the identified quality attributes to see how they could serve as a basis for an open software ecosystem platform with the conclusion that while none of them is a perfect fit they all have fundamental mechanisms necessary for an open software ecosystem approach.

  • 3.
    Felderer, Michael
    et al.
    Universität Innsbruck, Austria; Blekinge Institute of Technology, Sweden.
    Olsson Holmström, Helena
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Rabiser, Rick
    Johannes Kepler University Linz, Austria.
    Introduction to the special issue on quality engineering and management of software-intensive systems2019In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 149, p. 533-534Article in journal (Other academic)
  • 4.
    Figalist, Iris
    et al.
    Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany..
    Elsner, Christoph
    Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Breaking the vicious circle: A case study on why AI for software analytics and business intelligence does not take off in practice2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 184, article id 111135Article in journal (Refereed)
    Abstract [en]

    In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often remains at a prototypical stage, and the results are rarely used to make decisions based on data. To understand the underlying causes of this phenomenon, we conduct an explanatory case study consisting of and interview study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical AI-based analytics to continuous and productively usable software analytics and business intelligence solutions. In order to break the vicious circle in a targeted manner, we identify a set of solutions based on existing literature as well as the previously conducted interviews and survey. Finally, these solutions are validated by a focus group of experts. (C) 2021 Elsevier Inc. All rights reserved.

  • 5.
    Linåker, Johan
    et al.
    Lund University.
    Munir, Hussan
    Lund University.
    Wnuk, Krzysztof
    Blekinge Institute of Technolog.
    Mols, Carl-Eric
    Sony Mobile, Lund.
    Motivating the contributions: An Open Innovation perspective on what to share as Open Source Software2018In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 135, p. 17-36Article in journal (Refereed)
    Abstract [en]

    Open Source Software (OSS) ecosystems have reshaped the ways how software-intensive firms develop products and deliver value to customers. However, firms still need support for strategic product planning in terms of what to develop internally and what to share as OSS. Existing models accurately capture commoditization in software business, but lack operational support to decide what contribution strategy to employ in terms of what and when to contribute. This study proposes a Contribution Acceptance Process (CAP) model from which firms can adopt contribution strategies that align with product strategies and planning. In a design science influenced case study executed at Sony Mobile, the CAP model was iteratively developed in close collaboration with the firm’s practitioners. The CAP model helps classify artifacts according to business impact and control complexity so firms may estimate and plan whether an artifact should be contributed or not. Further, an information meta-model is proposed that helps operationalize the CAP model at the organization. The CAP model provides an operational OI perspective on what firms involved in OSS ecosystems should share, by helping them motivate contributions through the creation of contribution strategies. The goal is to help maximize return on investment and sustain needed influence in OSS ecosystems.

  • 6.
    Munappy, Aiswarya Raj
    et al.
    Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Arpteg, Anders
    Peltar operat AI platform, Hollandargatan 17, S-11160 Stockholm, Sweden..
    Brinne, Bjoern
    Peltar operat AI platform, Hollandargatan 17, S-11160 Stockholm, Sweden..
    Data management for production quality deep learning models: Challenges and solutions2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 191, article id 111359Article in journal (Refereed)
    Abstract [en]

    Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of data cannot be compromised. This study aims to explore the data management challenges encountered by practitioners developing systems with DL components, identify the potential solutions from the literature and validate the solutions through a multiple case study. We identified 20 data management challenges experienced by DL practitioners through a multiple interpretive case study. Further, we identified 48 articles through a systematic literature review that discuss the solutions for the data management challenges. With the second round of multiple case study, we show that many of these solutions have limitations and are not used in practice due to a combination of four factors: high cost, lack of skill-set and infrastructure, inability to solve the problem completely, and incompatibility with certain DL use cases. Thus, data management for data-intensive DL models in production is complicated. Although the DL technology has achieved very promising results, there is still a significant need for further research in the field of data management to build high-quality datasets and streams that can be used for building production-ready DL systems. Furthermore, we have classified the data management challenges into four categories based on the availability of the solutions.(c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • 7.
    Nostro, Nicola
    et al.
    University of Florence, Italy.
    Spalazzese, Romina
    Malmö högskola, Faculty of Technology and Society (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Di Giandomenico, Felicita
    CNR of Pisa, Italy.
    Inverardi, Paola
    University of l'Aquila, Italy.
    Achieving functional and non functional interoperability through synthesized connectors2016In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 111, p. 185-199Article in journal (Refereed)
    Abstract [en]

    Our everyday life is pervaded by the use of a number of heterogeneous systems that are continuously and dynamically available in the networked environment to interoperate to achieve some goal. Goals may include both functional and non functional aspects and the evolving nature of such environment requires automated solutions as means to reach the needed level of flexibility. Achieving interoperability in such environment is a challenging problem. Even though some of such systems may in principle interact since they have compatible functionalities and similar interaction protocols, mismatches in their protocols and non functional issues arising from the environment may undermine their seamless interoperability. In this paper, we propose an approach for the automated synthesis of application layer connectors between heterogeneous Networked Systems (NSs) addressing both functional and some non functional interoperability. Our contributions are: (i) an automated connectors synthesis approach for NSs interoperability taking into account functional, performance and dependability aspects spanning pre-deployment time and run-time; (ii) a connector adaptation process, related to the performance and dependability aspects; and (iii) a stochastic model-based implementation of the performance and dependability analysis. In addition, we implemented, analysed, and critically discussed a case study.

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