Publikationer från Malmö universitet
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  • 1.
    Bosch, Jan
    et al.
    Software Center and Chalmers University of Technology.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Software Center.
    Brinne, Björn
    Peltarion.
    Crnkovic, Ivica
    Chalmers Artificial Intelligence Research Center and Chalmers University of Technology.
    AI Engineering: Realizing the Potential of AI2022Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 39, nr 6, s. 23-27Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Artificial Intelligence (AI) engineering is an engineering discipline that is concerned with all aspects of development and evolution of AI systems (that is, systems that include AI components). AI engineering is primarily an extension of software engineering, but it also includes methods and technologies from data science and AI in general.

  • 2.
    Bosch, Jan
    et al.
    Chalmers University.
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Toward Evidence-Based Organizations Lessons from Embedded Systems, Online Games, and the Internet of Things2017Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 34, nr 5, s. 60-66Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Case studies investigated how companies in three domains transition to data-driven development. The results led to a model of the levels that software-intensive companies move through as they evolve from an opinionbased to an evidence-based organization.

  • 3.
    Ciccozzi, Federico
    et al.
    Mälardalen University, Sweden.
    Crnkovic, Ivica
    Chalmers University of Technology, University of Gothenburg, Sweden.
    Di Ruscio, Davide
    University of L'Aquila, Italy.
    Malavolta, Ivano
    Vrije Universiteit Amsterdam, Netherlands.
    Pelliccione, Patrizio
    Chalmers University of Technology, University of Gothenburg, Sweden.
    Spalazzese, Romina
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Model-Driven Engineering for Mission-Critical IoT Systems2017Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 34, nr 1, s. 46-53Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Mission-critical Internet of Things (MC-IoT) systems involve heterogeneous things from both the digital and physical worlds. They run applications whose failure might cause significant and possibly dramatic consequences, such as interruption of public services, significant business losses, and deterioration of enterprise operations. These applications require not only high availability, reliability, safety, and security but also regulatory compliance, scalability, and serviceability. At the same time, they're exposed to various facets of uncertainty, spanning from software and hardware variability to mission planning and execution in possibly unforeseeable environments. Model-driven engineering can potentially meet these challenges and better enable the adoption of MC-IoT systems.

  • 4.
    Fabijan, Aleksander
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dmitriev, Pavel
    Outreach, Seattle, WA, United States.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Software Engineering, Chalmers University of Technology, Goteborg, Sweden.
    The Online Controlled Experiment Lifecycle2020Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 37, nr 2, s. 60-67Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Online Controlled Experiments (OCEs) enable an accurate understanding of customer value and generate millions of dollars of additional revenue at Microsoft. Unlike other techniques for learning from customers, OCEs establish an accurate and causal relationship between a change and the impact observed. Although previous research describes technical and statistical dimensions, the key phases of online experimentation are not widely known, their impact and importance are obscure, and how to establish OCEs in an organization is underexplored. In this paper, using a longitudinal in-depth case study, we address this gap by (1) presenting the Experiment Lifecycle, and (2) demonstrating with four example experiments their profound impact. We show that OECs help optimize infrastructure needs and aid in project planning and measuring team efforts, in addition to their primary goal of accurately identifying what customers value. We conclude that product development should fully integrate the Experiment Lifecycle to benefit from the OCEs.

  • 5.
    Olsson Holmström, Helena
    et al.
    Malmö högskola, Teknik och samhälle (TS).
    Börjesson Sandberg, Anna
    Bosch, Jan
    Alahyari, Hiva
    Scale and Responsiveness in Large-Scale Software Development2014Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 31, nr 5, s. 87-93Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In large-scale software development, there is typically a conflict between being responsive to individual customers, while at the same time achieving scale in terms of delivering a high number of features to a large customer base. Most often, organizations focus on scale and individual customer requests are viewed as problematic since they add complexity to product variation and version control. Here, we explore the use of customer-specific teams as a means to address this conflict. First, we verify the use of customer-specific teams as successful for improving customer responsiveness, customer satisfaction and feature quality through a case study at Ericsson. Second, we identify three approaches for how to organize feature development, and recommendations on how software development companies can efficiently use these to improve their practices. Third, we observe new business opportunities that arise when using customer-specific teams.

  • 6.
    Spalazzese, Romina
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Pelliccione, Patrizio
    Computer Science and Engineering, Chalmers University of Technology, Sweden.
    Eklund, Ulrik
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    INTERO: an Interoperability Model for Large Systems2020Ingår i: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 37, nr 3, s. 38-45Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Interoperability is one of the key challenges in present and future software-intensive systems that are large, distributed, and increasingly built as integration of existing and third parties components or systems, of legacy parts, and of newly developed parts. Moreover, such systems evolve over time due to different reasons, e.g., features are added, changed or removed, new protocols are supported, standards are changed, refactoring.To help large companies identifying how to manage and improve interoperability among their evolving software systems, our objective is to develop an interoperability model for large systems by focusing on software development.Our method to conceive and evaluate the model is through a tight collaboration among two universities and five large international companies.The results of our work are the INTERO model and the INTERO evaluation framework. They permit to analyse the specific interoperability problem, to conceive strategies to enhance interoperability, and finally to re-evaluate the problem in order to understand whether there is an improvement in terms of software interoperability.

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