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  • 51.
    Fredriksson, Teodor
    et al.
    Chalmers University of Technology,Department of Computer Science and Engineering,Gothenburg,Sweden.
    Bosch, Jan
    Chalmers University of Technology,Department of Computer Science and Engineering,Gothenburg,Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mattos, David Issa
    Volvo Cars,Gothenburg,Sweden.
    Machine Learning Algorithms for Labeling: Where and How They are Used?2022Ingår i: 2022 IEEE International Systems Conference (SysCon), IEEE, 2022Konferensbidrag (Refereegranskat)
    Abstract [en]

    With the increased availability of new and better computer processing units (CPUs) as well as graphical processing units (GPUs), the interest in statistical learning and deep learning algorithms for classification tasks has grown exponentially. These classification algorithms often require the presence of fully labeled instances during the training period for maximum classification accuracy. However, in industrial applications, data is commonly not fully labeled, which both reduces the prediction accuracy of the learning algorithms as well as increases the project cost to label the missing instances. The purpose of this paper is to survey the current state-of-the-art literature on machine learning algorithms that are used for assisted or automatic labeling and to understand where these are used. We performed a systematic mapping study and identified 52 primary studies relevant to our research. This paper provides three main contributions. First, we identify the existing machine learning algorithms for labeling and we present a taxonomy of these algorithms. Second, we identify the datasets that are used to evaluate the algorithms and we provide a mapping of the datasets based on the type of data and the application area. Third, we provide a process to support people in industry to optimally label their dataset. The results presented in this paper can be used by both researchers and practitioners aiming to improve the missing labels with the aid of machine algorithms or to select appropriate datasets to compare new state-of-the art algorithms in their respective application area.

  • 52.
    Fredriksson, Teodor
    et al.
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Mattos, David Issa
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    An Empirical Evaluation of Algorithms for Data Labeling2021Ingår i: 2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021) / [ed] Chan, WK Claycomb, B Takakura, H Yang, JJ Teranishi, Y Towey, D Segura, S Shahriar, H Reisman, S Ahamed, SI, IEEE, 2021, s. 201-209Konferensbidrag (Refereegranskat)
    Abstract [en]

    The lack of labeled data is a major problem in both research and industrial settings since obtaining labels is often an expensive and time-consuming activity. In the past years, several machine learning algorithms were developed to assist and perform automated labeling in partially labeled datasets. While many of these algorithms are available in open-source packages, there is a lack of research that investigates how these algorithms compare to each other for different types of datasets and with different percentages of available labels. To address this problem, this paper empirically evaluates and compares seven algorithms for automated labeling in terms of their accuracy. We investigate how these algorithms perform in twelve different and well-known datasets with three different types of data, images, texts, and numerical values. We evaluate these algorithms under two different experimental conditions, with 10% and 50% labels of available labels in the dataset. Each algorithm, in each dataset for each experimental condition, is evaluated independently ten times with different random seeds. The results are analyzed and the algorithms are compared utilizing a Bayesian Bradley-Terry model. The results indicate that the active learning algorithms using the query strategies uncertainty sampling, QBC and random sampling are always the best algorithms. However, this comes with the expense of increased manual labeling effort. These results help machine learning practitioners in choosing optimal machine learning algorithms to label their data.

  • 53.
    Fredriksson, Teodor
    et al.
    Chalmers University of Technology, Hörselgången 11, 417 56, Gothenburg, Sweden.
    Mattos, David Issa
    Chalmers University of Technology, Hörselgången 11, 417 56, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Hörselgången 11, 417 56, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Data Labeling: An Empirical Investigation into Industrial Challenges and Mitigation Strategies2020Ingår i: Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020, Proceedings / [ed] Maurizio Morisio; Marco Torchiano; Andreas Jedlitschka, Springer, 2020, s. 202-216Konferensbidrag (Refereegranskat)
    Abstract [en]

    Labeling is a cornerstone of supervised machine learning. However, in industrial applications, data is often not labeled, which complicates using this data for machine learning. Although there are well-established labeling techniques such as crowdsourcing, active learning, and semi-supervised learning, these still do not provide accurate and reliable labels for every machine learning use case in the industry. In this context, the industry still relies heavily on manually annotating and labeling their data. This study investigates the challenges that companies experience when annotating and labeling their data. We performed a case study using a semi-structured interview with data scientists at two companies to explore their problems when labeling and annotating their data. This paper provides two contributions. We identify industry challenges in the labeling process, and then we propose mitigation strategies for these challenges.

  • 54.
    Gerostathopoulos, Ilias
    et al.
    Technical University Munich, Munich, Germany.
    Konersmann, Marco
    University of Koblenz-Landau, Mainz, Germany.
    Krusche, Stephan
    Technical University Munich, Munich, Germany.
    Mattos, David I.
    Chalmers University of Technology, Goteborg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Goteborg, Sweden.
    Bures, Tomas
    Charles University in Prague, Czech Rep.
    Fitzgerald, Brian
    University of Limerick, Limerick , Ireland.
    Goedicke, Michael
    University of Duisburg-Essen, Duisburg, Germany.
    Muccini, Henry
    University of L'Aquila, L'Aquila, Italy.
    Olsson, Helena H.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Brand, Thomas
    University of Potsdam, Potsdam, Germany.
    Chatley, Robert
    Imperial College London, London, England UK.
    Diamantopoulos, Nikolaos
    Independent.
    Friedman, Arik
    Atlassian, Sydney, Australia.
    Jiménez, Miguel
    University of Victoria, Victoria, Canada.
    Johanssen, Jan Ole
    Technical University Munich, Munich, Germany.
    Manggala, Putra
    Shopify, Canada.
    Koseki, Masumi
    Hitachi, Tokyo, Japan.
    Melegati, Jorge
    Free University of Bozen-Bolzano, Bolzano, Italy.
    Munaiah, Nuthan
    Rochester Institute of Technology, Rochester, NY, USA.
    Tamura, Gabriel
    Universidad Icesi, Cauca, Colombia.
    Theodorou, Vasileios
    Intracom Telecom, Athens, Greece.
    Wong, Jeffrey
    Netflix, Los Gatos, CA, USA.
    Figalist, Iris
    Siemens, Munich, Germany.
    Continuous Data-driven Software Engineering: Towards a Research Agenda2019Ingår i: Software Engineering Notes: an Informal Newsletter of The Specia, ISSN 0163-5948, E-ISSN 1943-5843, Vol. 44, nr 3, s. 60-64Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.  

     

  • 55.
    Green, Rolf
    et al.
    Chalmers University of Technology.
    Bosch, Jan
    Chalmers University of Technology.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Autonomously Improving Systems in Industry: A Systematic Literature Review2021Ingår i: Software Business: 11th International Conference, ICSOB 2020, Karlskrona, Sweden, November 16–18, 2020, Proceedings / [ed] Eriks Klotins; Krzysztof Wnuk, Springer, 2021, s. 30-45Konferensbidrag (Refereegranskat)
    Abstract [en]

    A significant amount of research effort is put into studying machine learning (ML) and deep learning (DL) technologies. Real-world ML applications help companies to improve products and automate tasks such as classification, image recognition and automation. However, a traditional “fixed” approach where the system is frozen before deployment leads to a sub-optimal system performance. Systems autonomously experimenting with and improving their own behavior and performance could improve business outcomes but we need to know how this could actually work in practice. While there is some research on autonomously improving systems, the focus on the concepts and theoretical algorithms. However, less research is focused on empirical industry validation of the proposed theory. Empirical validations are usually done through simulations or by using synthetic or manually alteration of datasets. The contribution of this paper is twofold. First, we conduct a systematic literature review in which we focus on papers describing industrial deployments of autonomously improving systems and their real-world applications. Secondly, we identify open research questions and derive a model that classifies the level of autonomy based on our findings in the literature review. 

  • 56.
    Hegazy, Shady
    et al.
    Siemens Technology, Munich, Germany.
    Elsner, Christoph
    Siemens Technology, Munich, Germany.
    Bosch, Jan
    Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Analytics and Data-Driven Methods and Practices in Platform Ecosystems: a systematic literature review2023Ingår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    The emergence of platform ecosystems has transformed the business landscape in many industries, giving rise to novel modes of interorganizational cooperation and value co-creation, as well as unconventional challenges. The vast traces of data generated by platform ecosystems makes them ripe for the use of analytics and data-driven methods aimed at improving their health, performance, business outcomes, and evolution. However, the research on the application of analytics within platform ecosystems is limited and spread across multiple disciplines. To address this gap, we conducted a systematic literature review on the application of analytics and data-driven methods and practices within platform ecosystems. A total of 56 studies were reviewed, and underwent data extraction, analysis, and synthesis processes. In addition to presenting themes and patterns in the recent and relevant literature on platform ecosystems analytics, our review offers the following outcomes: an actionable overview of the analytics toolbox currently used within platform ecosystems—spanning domains such as machine learning, deep learning, data science, modelling, simulation, among others—; a roadmap for practitioners to achieve analytics maturity; and a summary of underexplored research areas.

  • 57.
    Hyrynsalmi, Sami
    et al.
    LUT University,Dept. Software Engineering,Lahti,Finland.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology,Dept. Computer Science and Engineering,Göteborg,Sweden.
    Towards a Data Business Maturity Model for Software-intensive Embedded System Companies2023Ingår i: 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive companies.This multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses.

  • 58.
    Hyrynsalmi, Sami
    et al.
    LUT University, Mukkulankatu 19, 15210, Lahti, Finland.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology, Hörselgången 11, 412 96, Göteborg, Sweden.
    Hyrynsalmi, Sonja
    LUT University, Mukkulankatu 19, 15210, Lahti, Finland.
    Quō vādis, Data Business?: A Study for Understanding Maturity of Embedded System Companies in Data Economy2022Ingår i: Software Business: 13th International Conference, ICSOB 2022, Bolzano, Italy, November 8–11, 2022, Proceedings / [ed] Noel Carroll; Anh Nguyen-Duc; Xiaofeng Wang; Viktoria Stray, Springer, 2022, s. 141-148Konferensbidrag (Refereegranskat)
    Abstract [en]

    Data has been claimed to be the new oil of the 21st century as it has seen to be able both to improve the existing products and services as well as to create new revenue streams for its utilizing company with a secondary customers base. However, while there is active streams of research for developing machine learning and data science methods, considerably less has been done to understand and characterize data business activities in the software-intensive companies. This study uses a multiple case study approach in the software-intensive embedded system domain. Four large international embedded system companies were selected as the case study subjects. The objective is to understand how the case companies are developing their activities for successful utilization of the data. The study identifies six distinct stages with their own challenges. In addition, this study serves as a starting for further work for supporting software-intensive embedded system companies to start data business.

  • 59.
    Hyrynsalmi, Sonja M.
    et al.
    Dept. Software Engineering, LUT University, Lahti, Finland.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    Towards an integration management maturity2023Ingår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside organizations.This research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration solutions.To address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management.

  • 60.
    Issa Mattos, David
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Dakkak, Anas
    Ericsson AB, Stockholm, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    The HURRIER process for experimentation in business-to-business mission-critical systems2023Ingår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 35, nr 5, artikel-id e2390Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Continuous experimentation (CE) refers to a set of practices used by software companies to rapidly assess the usage, value, and performance of deployed software using data collected from customers and systems in the field using an experimental methodology. However, despite its increasing popularity in developing web-facing applications, CE has not been studied in the development process of business-to-business (B2B) mission-critical systems. By observing the CE practices of different teams, with a case study methodology inside Ericsson, we were able to identify the different practices and techniques used in B2B mission-critical systems and a description and classification of the four possible types of experiments. We present and analyze each of the four types of experiments with examples in the context of the mission-critical long-term evolution (4G) product. These examples show the general experimentation process followed by the teams and the use of the different CE practices and techniques. Based on these examples and the empirical data, we derived the HURRIER process to deliver high-quality solutions that the customers value. Finally, we discuss the challenges, opportunities, and lessons learned from applying CE and the HURRIER process in B2B mission-critical systems. 

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  • 61. Johansson, Enrico
    et al.
    Bergdahl, Daniel
    Bosch, Jan
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Quantitative Requirements Prioritization from a Pre-development Perspective2015Ingår i: Software Process Improvement and Capability Determination: 15th International Conference, SPICE 2015, Gothenburg, Sweden, June 16-17, 2015. Proceedings, Springer, 2015, s. 58-71Konferensbidrag (Refereegranskat)
    Abstract [en]

    Feature content in system releases tends to be prioritized using limited amounts of qualitative user input and based on the opinions of those in product management. This leads to several problems due to the wasteful allocation of R&D resources. In this paper, we present the results of our efforts to collect quantitative customer input before the start of development using mock-ups and surveys for a mobile application developed by Sony Mobile. Our research shows that (1) collecting quantitative feedback before development is feasible, (2) the data collected deviates from the original feature prioritization, i.e. it is beneficial and (3) the data gives further insight in requirement prioritization than a qualitative method could have provided.

  • 62.
    Johansson, Enrico
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bergdahl, Daniel
    Bosch, Jan
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Requirement Prioritization with Quantitative Data: A Case Study2015Ingår i: Product-Focused Software Process Improvement: 16th International Conference, PROFES 2015, Bolzano, Italy, December 2-4, 2015, Proceedings, Springer, 2015, s. 89-104Konferensbidrag (Refereegranskat)
    Abstract [en]

    Feature content in system releases tends to be prioritized using limited amounts of qualitative user input and based on the opinions of those in product management. This leads to several problems including the wasteful allocation of R&D resources. In this paper, we present the results of our efforts to collect quantitative customer input before the start of development using a mock-up for a mobile application developed by Sony Mobile Communications Inc. Our research shows that (1) product managers change their prioritization when quantitative data is presented to them; (2) product managers change their prioritization which is converged to the prioritization indicated by the quantitative data (3) the quantitative data is regarded as beneficial by the product managers.

  • 63.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Gillblad, Daniel
    Chalmers University of Technology & AI Sweden,Gothenburg,Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden.
    Advancing MLOps from Ad hoc to Kaizen2023Ingår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.

  • 64.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, J.
    Chalmers University of Technology.
    Towards MLOps: A Framework and Maturity Model2021Ingår i: Proceedings - 2021 47th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2021, IEEE, 2021, s. 334-341Konferensbidrag (Refereegranskat)
    Abstract [en]

    The adoption of continuous software engineering practices such as DevOps (Development and Operations) in business operations has contributed to significantly shorter software development and deployment cycles. Recently, the term MLOps (Machine Learning Operations) has gained increasing interest as a practice that brings together data scientists and operations teams. However, the adoption of MLOps in practice is still in its infancy and there are few common guidelines on how to effectively integrate it into existing software development practices. In this paper, we conduct a systematic literature review and a grey literature review to derive a framework that identifies the activities involved in the adoption of MLOps and the stages in which companies evolve as they become more mature and advanced. We validate this framework in three case companies and show how they have managed to adopt and integrate MLOps in their large-scale software development companies. The contribution of this paper is threefold. First, we review contemporary literature to provide an overview of the state-of-the-art in MLOps. Based on this review, we derive an MLOps framework that details the activities involved in the continuous development of machine learning models. Second, we present a maturity model in which we outline the different stages that companies go through in evolving their MLOps practices. Third, we validate our framework in three embedded systems case companies and map the companies to the stages in the maturity model. 

  • 65.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University.
    AI Deployment Architecture: Multi-Case Study for Key Factor Identification2020Ingår i: 2020 27th Asia-Pacific Software Engineering Conference (APSEC), IEEE, 2020, Vol. 1, s. 395-404Konferensbidrag (Refereegranskat)
    Abstract [en]

    Machine learning and deep learning techniques are becoming increasingly popular and critical for companies as part of their systems. However, although the development and prototyping of ML/DL systems are common across companies, the transition from prototype to production-quality deployment models are challenging. One of the key challenges is how to determine the selection of an optimal architecture for AI deployment. Based on our previous research, and to offer support and guidance to practitioners, we developed a framework in which we present five architectural alternatives for AI deployment ranging from centralized to fully decentralized edge architectures. As part of our research, we validated the framework in software-intensive embedded system companies and identified key challenges they face when deploying ML/DL models. In this paper, and to further advance our research on this topic, we identify factors that help practitioners determine what architecture to select for the ML/D L model deployment. For this, we conducted a follow-up study involving interviews and workshops in seven case companies in the embedded systems domain. Based on our findings, we identify three key factors and develop a framework in which we outline how prioritization and trade-offs between these result in certain architecture. The contribution of the paper is threefold. First, we identify key factors critical for AI system deployment. Second, we present the architecture selection framework that explains how prioritization and trade-offs between key factors result in the selection of a certain architecture. Third, we discuss additional factors that may or may not influence the selection of an optimal architecture.

  • 66.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology.
    AI on the Edge: Architectural Alternatives2020Ingår i: Proceedings 46th Euromicro Conferenceon Software Engineering and Advanced Applications SEAA 2020 / [ed] Antonio Martini, Manuel Wimmer, Amund Skavhaug, IEEE, 2020, s. 21-28Konferensbidrag (Refereegranskat)
    Abstract [en]

    Since the advent of mobile computing and IoT, a large amount of data is distributed around the world. Companies are increasingly experimenting with innovative ways of implementing edge/cloud (re)training of AI systems to exploit large quantities of data to optimize their business value. Despite the obvious benefits, companies face challenges as the decision on how to implement edge/cloud (re)training depends on factors such as the task intent, the amount of data needed for (re)training, edge-to-cloud data transfer, the available computing and memory resources. Based on action research in a software-intensive embedded systems company where we study multiple use cases as well as insights from our previous collaborations with industry, we develop a generic framework consisting of five architectural alternatives to deploy AI on the edge utilizing transfer learning. We validate the framework in four additional case companies and present the challenges they face in selecting the optimal architecture. The contribution of the paper is threefold. First, we develop a generic framework consisting of five architectural alternatives ranging from a centralized architecture where cloud (re)training is given priority to a decentralized architecture where edge (re)training is instead given priority. Second, we validate the framework in a qualitative interview study with four additional case companies. As an outcome of validation study, we present two variants to the architectural alternatives identified as part of the framework. Finally, we identify the key challenges that experts face in selecting an ideal architectural alternative.

  • 67.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology.
    Architecting AI Deployment: A Systematic Review of State-of-the-art and State-of-practice Literature2020Ingår i: Software Business: 11th International Conference, ICSOB 2020, Karlskrona, Sweden, November 16–18, 2020, Proceedings / [ed] Eriks Klotins; Krzysztof Wnuk, Springer, 2020, s. 14-29Konferensbidrag (Refereegranskat)
    Abstract [en]

    Companies across domains are rapidly engaged in shifting computational power and intelligence from centralized cloud to fully decentralized edges to maximize value delivery, strengthen security and reduce latency. However, most companies have only recently started pursuing this opportunity and are therefore at the early stage of the cloud-to-edge transition. To provide an overview of AI deployment in the context of edge/cloud/hybrid architectures, we conduct a systematic literature review and a grey literature review. To advance understanding of how to integrate, deploy, operationalize and evolve AI models, we derive a framework from existing literature to accelerate the end-to-end deployment process. The framework is organized into five phases: Design, Integration, Deployment, Operation and Evolution. We make an attempt to analyze the extracted results by comparing and contrasting them to derive insights. The contribution of the paper is threefold. First, we conduct a systematic literature review in which we review the contemporary scientific literature and provide a detailed overview of the state-of-the-art of AI deployment. Second, we review the grey literature and present the state-of-practice and experience of practitioners while deploying AI models. Third, we present a framework derived from existing literature for the end-to-end deployment process and attempt to compare and contrast SLR and GLR results.

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  • 68.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology.
    Developing ML/DL Models: A Design Framework2020Ingår i: Proceedings 2020 IEEE/ACM International Conferenceon Software and System Processes ICSSP 2020, ACM Digital Library, 2020, s. 1-10Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.

  • 69.
    John, Meenu Mary
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Towards an AI-driven business development framework: A multi-case study2023Ingår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 35, nr 6, artikel-id e2432Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.

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  • 70. Karvonen, Teemu
    et al.
    Lwakatare, Lucy Ellen
    Sauvola, Tanja
    Bosch, Jan
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Kuvaja, Pasi
    Oivo, Markku
    Hitting the Target: Practices for Moving Toward Innovation Experiment Systems2015Ingår i: Software Business: 6th International Conference, ICSOB 2015, Braga, Portugal, June 10-12, 2015, Proceedings, Springer, 2015, s. 117-131Konferensbidrag (Refereegranskat)
    Abstract [en]

    The benefits and barriers that software development companies face when moving beyond agile development practices are identified in a multiple-case study in five Finnish companies. The practices that companies need to adopt when moving towards innovation experiment systems are recognised. The background of the study is the Stairway to Heaven (StH) model that describes the path that many software development companies take when advancing their development practices. The development practices in each case are investigated and analysed in relation to the StH model. At first the results of the analysis strengthened the validity of the StH model as a path taken by software development companies to advance their development practices. Based on the findings, the StH model was extended with a set of additional practices and their adoption levels for each step of the model. The extended model was validated in five case companies.

  • 71.
    Konersmann, Marco
    et al.
    University of Koblenz-Landau, Mainz, Germany.
    Fitzgerald, Brian
    University of Limerick, Limerick, Ireland.
    Goedicke, Michael
    University of Duisburg-Essen, Duisburg, Germany.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    University Gothenburg, Gothenburg, Sweden.
    Krusche, Stephan
    Technische Universität München, München, Germany.
    Rapid Continuous Software Engineering - State of the Practice and Open Research Questions2021Ingår i: Software Engineering Notes: an Informal Newsletter of The Specia, ISSN 0163-5948, E-ISSN 1943-5843, Vol. 46, nr 1, s. 25-27Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    We need to built software rapidly and with a high quality. These goals seem to be contradictory, but actually, implementing automation in build and deployment procedures as well as quality analysis can improve both the development pace and the resulting quality at the same time. Rapid Continuous Software Engineering describes novel software engineering approaches that focus on short release cycles, continuous deployment, delivery, and continuous improvement through rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support high pace development. This paper reports on the results of the 6th International Workshop on Rapid Continuous Software Engineering (RCoSE 2020), which focuses on the challenges and potential solutions in the area of Rapid Continuous Software Engineering, before reporting on our discussions regarding the state of the practice and open research topics.  

     

  • 72.
    Liu, Yuchu
    et al.
    Volvo Cars, Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lantz, Jonn
    Volvo Cars, Gothenburg, Sweden..
    An architecture for enabling A/B experiments in automotive embedded software2021Ingår i: 2021 IEEE 45TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2021) / [ed] Chan, WK Claycomb, B Takakura, H Yang, JJ Teranishi, Y Towey, D Segura, S Shahriar, H Reisman, S Ahamed, SI, IEEE, 2021, s. 992-997Konferensbidrag (Refereegranskat)
    Abstract [en]

    A/B experimentation is a known technique for datadriven product development and has demonstrated its value in web-facing businesses. With the digitalisation of the automotive industry, the focus in the industry is shifting towards software. For automotive embedded software to continuously improve, A/B experimentation is considered an important technique. However, the adoption of such a technique is not without challenge. In this paper, we present an architecture to enable A/B testing in automotive embedded software. The design addresses challenges that are unique to the automotive industry in a systematic fashion. Going from hypothesis to practice, our architecture was also applied in practice for running online experiments on a considerable scale. Furthermore, a case study approach was used to compare our proposal with state-of-practice in the automotive industry. We found our architecture design to be relevant and applicable in the efforts of adopting continuous A/B experiments in automotive embedded software.

  • 73.
    Liu, Yuchu
    et al.
    Volvo Cars, Gothenburg, Sweden.
    Mattos, David Issa
    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, Jan
    Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lantz, Jonn
    Volvo Cars, Gothenburg, Sweden.
    Bayesian propensity score matching in automotive embedded software engineering2021Ingår i: 2021 28th Asia-Pacific Software Engineering Conference (APSEC), IEEE, 2021Konferensbidrag (Refereegranskat)
    Abstract [en]

    Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.

  • 74.
    Liu, Yuchu
    et al.
    Volvo Cars, Gothenburg, Sweden..
    Mattos, David Issa
    Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö Univ, Comp Sci & Media Technol, Malmö, Sweden..
    Lantz, Jonn
    Volvo Cars, Gothenburg, Sweden..
    Size matters? Or not: A/B testing with limited sample in automotive embedded software2021Ingår i: 2021 47TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2021) / [ed] Baldassarre, MT Scanniello, G Skavhaug, A, IEEE, 2021, s. 300-307Konferensbidrag (Refereegranskat)
    Abstract [en]

    A/B testing is gaining attention in the automotive sector as a promising tool to measure casual effects from software changes. Different from the web-facing businesses, where A/B testing has been well-established, the automotive domain often suffers from limited eligible users to participate in online experiments. To address this shortcoming, we present a method for designing balanced control and treatment groups so that sound conclusions can be drawn from experiments with considerably small sample sizes. While the Balance Match Weighted method has been used in other domains such as medicine, this is the first paper to apply and evaluate it in the context of software development. Furthermore, we describe the Balance Match Weighted method in detail and we conduct a case study together with an automotive manufacturer to apply the group design method in a fleet of vehicles. Finally, we present our case study in the automotive software engineering domain, as well as a discussion on the benefits and limitations of the A/B group design method.

  • 75.
    Lwakatare, Lucy Ellen
    et al.
    University of Oulu, Finland.
    Karvonen, Teemu
    University of Oulu, Finland.
    Sauvola, Tanja
    University of Oulu, Finland.
    Kuvaja, Pasi
    University of Oulu, Finland.
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Bosch, Jan
    Chalmers University of Technology, Sweden.
    Oivo, Markku
    University of Oulu, Finland.
    Towards DevOps in the Embedded Systems Domain: Why is It so Hard?2016Ingår i: 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE, 2016, s. 5437-5446Konferensbidrag (Refereegranskat)
    Abstract [en]

    DevOps is a predominant phenomenon in the web domain. Its two core principles emphasize collaboration between software development and operations, and the use of agile principles to manage deployment environments and their configurations. DevOps techniques, such as collaboration and behaviour-driven monitoring, have been used by web companies to facilitate continuous deployment of new functionality to customers. The techniques may also offer opportunities for continuous product improvement when adopted in the embedded systems domain. However, certain characteristics of embedded software development present obstacles for DevOps adoption, and as yet, there is no empirical evidence of its adoption in the embedded systems domain. In this study, we present the challenges for DevOps adoption in embedded systems using a multiple-case study approach with four companies. The contribution of this paper is to introduce the concept of DevOps adoption in the embedded systems domain and then to identify key challenges for the DevOps adoption.

  • 76.
    Lwakatare, Lucy Ellen
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Raj, Aiswarya
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Crnkovic, Ivica
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions2020Ingår i: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 127, artikel-id 106368Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Background : Developing and maintaining large scale machine learning (ML) based software systems in an in-dustrial setting is challenging. There are no well-established development guidelines, but the literature contains reports on how companies develop and maintain deployed ML-based software systems. Objective : This study aims to survey the literature related to development and maintenance of large scale ML -based systems in industrial settings in order to provide a synthesis of the challenges that practitioners face. In addition, we identify solutions used to address some of these challenges. Method : A systematic literature review was conducted and we identified 72 papers related to development and maintenance of large scale ML-based software systems in industrial settings. The selected articles were qualita-tively analyzed by extracting challenges and solutions. The challenges and solutions were thematically synthe-sized into four quality attributes: adaptability, scalability, safety and privacy. The analysis was done in relation to ML workflow, i.e. data acquisition, training, evaluation, and deployment. Results : We identified a total of 23 challenges and 8 solutions related to development and maintenance of large scale ML-based software systems in industrial settings including six different domains. Challenges were most often reported in relation to adaptability and scalability. Safety and privacy challenges had the least reported solutions. Conclusion : The development and maintenance on large-scale ML-based systems in industrial settings introduce new challenges specific for ML, and for the known challenges characteristic for these types of systems, require new methods in overcoming the challenges. The identified challenges highlight important concerns in ML system development practice and the lack of solutions point to directions for future research.

  • 77.
    Lwakatare, Lucy
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Raj, Aiswarya
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Crnkovic, Ivica
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    A taxonomy of software engineering challenges for machine learning systems: An empirical investigation2019Ingår i: Agile Processes in Software Engineering and Extreme Programming: 20th International Conference, XP 2019, Montréal, QC, Canada, May 21–25, 2019, Proceedings, Springer, 2019, s. 227-243Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial intelligence enabled systems have been an inevitable part of everyday life. However, efficient software engineering principles and processes need to be considered and extended when developing AI- enabled systems. The objective of this study is to identify and classify software engineering challenges that are faced by different companies when developing software-intensive systems that incorporate machine learning components. Using case study approach, we explored the development of machine learning systems from six different companies across various domains and identified main software engineering challenges. The challenges are mapped into a proposed taxonomy that depicts the evolution of use of ML components in software-intensive system in industrial settings. Our study provides insights to software engineering community and research to guide discussions and future research into applied machine learning.

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  • 78.
    Mattos, D. I.
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, J.
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Statistical Models for the Analysis of Optimization Algorithms with Benchmark Functions2021Ingår i: IEEE Transactions on Evolutionary Computation, ISSN 1089-778X, E-ISSN 1941-0026, Vol. 25, nr 6, s. 1163-1177Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Frequentist statistical methods, such as hypothesis testing, are standard practices in studies that provide benchmark comparisons. Unfortunately, these methods have often been misused, e.g., without testing for their statistical test assumptions or without controlling for familywise errors in multiple group comparisons, among several other problems. Bayesian data analysis (BDA) addresses many of the previously mentioned shortcomings but its use is not widely spread in the analysis of empirical data in the evolutionary computing community. This article provides three main contributions. First, we motivate the need for utilizing BDA and provide an overview of this topic. Second, we discuss the practical aspects of BDA to ensure that our models are valid and the results are transparent. Finally, we provide five statistical models that can be used to answer multiple research questions. The online Appendix provides a step-by-step guide on how to perform the analysis of the models discussed in this article, including the code for the statistical models, the data transformations, and the discussed tables and figures. 

  • 79.
    Mattos, David Issa
    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 universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    ACE: Easy Deployment of Field Optimization Experiments2019Ingår i: SOFTWARE ARCHITECTURE, ECSA 2019 / [ed] Bures, T Duchien, L Inverardi, P, Springer, 2019, s. 264-279Konferensbidrag (Refereegranskat)
    Abstract [en]

    Optimization of software parameters is a recurring activity in the life-cycle of many software products, from prototypes and simulations, test beds and hardware-in-the-loop scenarios, field calibrations to the evolution of continuous deployment cycles. To perform this activity, software companies require a combination of software developers and optimization experts with domain specific knowledge. Moreover, in each of life-cycle steps, companies utilize a plethora of different tools, tailored for specific domains or development stages. To most companies, this scenario leads to an excessive cost in the optimization of smaller features or in cases where it is not clear what the returned value will be. In this work we present a new optimization system based on field experiments, that is aimed to facilitate the adoption of optimization in all stages of development. We provide two main contributions. First, we present the architecture of a new optimization system that allows existing software systems to perform optimization procedures in different domains and in different development stages. This optimization system utilizes domain-agnostic interfaces to allow existing systems to perform optimization procedures with minimal invasiveness and optimization expertise. Second, we provide an overview of the deployments, discuss the advantages and limitations and evaluate the optimization system in three empirical scenarios: (1) offline optimization with simulations; (2) optimization of a communication system in a test bed in collaboration with Ericsson; (3) live optimization of a mobile application in collaboration with Sony Mobile. We aim to provide practitioners with a single optimization tool that can leverage their optimization activities from offline to live systems, with minimal invasiveness and optimization expertise.

  • 80.
    Mattos, David Issa
    et al.
    Chalmers.
    Bosch, Jan
    Chalmers.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dakkak, Anas
    Ericsson.
    Bergh, Krister
    Ericsson.
    Automated Optimization of Software Parameters in a Long Term Evolution Radio Base Station2019Ingår i: 2019 IEEE International Systems Conference (SysCon), Institute of Electrical and Electronics Engineers (IEEE), 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    Radio network optimization is concerned with the configuration of radio base station parameters in order to achieve the desired level of service quality in addition to many other differentiating technical factors. Mobile network operators have different physical locations, levels of traffic profiles, number of connected devices, and the desired quality of service. All of these conditions make the problem of optimizing the parameters of a radio base station specific to the operator's business goals. The high number of calibration parameters and the complex interaction between them make the system behave as a black-box model for any practical purpose. The computation of relevant operator metrics is often stochastic, and it can take several minutes to compute the effect of changing a single, making it impractical to optimize systems with approaches that require a large number of iterations. Operators want to optimize their already deployed system in online scenarios while minimizing the exposure of the system to a negative set of parameters during the optimization procedure. This paper presents a novel approach to the optimization of a Long Term Evolution (LTE) radio base station in a large search space with an expensive stochastic objective and a limited regret bounds scenario. We show the feasibility of this approach by implementing it in an industrial testing bed radio base station connected to real User Equipment (UE) in collaboration with Ericsson. Two optimization processes in this experimental setup are executed to show the feasibility of the approach in real-world scenarios.

  • 81.
    Mattos, David Issa
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Korshani, Aita Maryam
    Volvo Cars, Gothenburg, Sweden.
    Lantz, John
    Volvo Cars, Gothenburg, Sweden.
    Automotive A/B testing: Challenges and Lessons Learned from Practice2020Ingår i: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2020, s. 101-109Konferensbidrag (Refereegranskat)
    Abstract [en]

    Over the past 15 years, A/B testing has been a critical tool for accurate prioritization of development efforts in online and web-facing companies. As automotive companies progress on their digitalization process, A/B testing and other experimentation techniques start to be adopted. However, specific characteristics of the automotive software industry create additional challenges to the successful adoption of A/B testing. Recently, research has been conducted to investigate the challenges and opportunities for experimentation techniques in the automotive and more generally in the embedded systems domain. However, despite the collaboration with industry, previous research was based on either hypothesized or toy scenarios in companies seeking, but not yet running experimentation. Utilizing a case study method, we investigate the challenges of adopting A/B testing in two large-scale automotive companies that are currently running or preparing for their first A/B testing. The contribution of this paper is two-fold. First, we present our main findings in terms of the challenges of real A/B testing iterations in automotive vehicles. Second, we present the current, potential solutions and lessons learned from applying A/B testing in the automotive domain.

  • 82.
    Mattos, David Issa
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    More for Less: Automated Experimentation in Software-Intensive Systems2017Ingår i: Product-Focused Software Process Improvement, Springer, 2017, s. 146-161Konferensbidrag (Refereegranskat)
    Abstract [en]

    Companies developing autonomous and software-intensive systems show an increasing need to adopt experimentation and data-driven strategies in their development process. With the growing complexity of the systems, companies are increasing their data analytic and experimentation teams to support data-driven development. However, organizations cannot increase in size at the same pace as the system complexity grows. Experimentation teams could run a larger number of experiments by letting the system itself to coordinate its own experiments, instead of the humans. This process is called automated experimentation. However, currently, no tools or frameworks address the challenge of running automated experiments. This paper discusses, through a set of architectural design decisions, the development of an architecture framework that supports automated continuous experiments. The contribution of this paper is twofold. First, it presents, through a set of architectural design decisions, an architecture framework for automated experimentation. Second, it evaluates the architecture framework experimentally in the context of a human-robot interaction proxemics distance problem. This automated experimentation framework aims to deliver more value from the experiments while using fewer R&D resources.

  • 83.
    Mattos, David Issa
    et al.
    Chalmers Univ Technol, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers Univ Technol, Gothenburg, Sweden.
    Olsson Holmström, Helena
    Malmö högskola, Fakulteten för teknik och samhälle (TS).
    Your System Gets Better Every Day You Use It: Towards Automated Continuous Experimentation2017Ingår i: 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2017, s. 256-265Konferensbidrag (Refereegranskat)
    Abstract [en]

    Innovation and optimization in software systems can occur from pre-development to post-deployment stages. Companies are increasingly reporting the use of experiments with customers in their systems in the post-deployment stage. Experiments with customers and users are can lead to a significant learning and return-on-investment. Experiments are used for both validation of manual hypothesis testing and feature optimization, linked to business goals. Automated experimentation refers to having the system controlling and running the experiments, opposed to having the R&D organization in control. Currently, there are no systematic approaches that combine manual hypothesis validation and optimization in automated experiments. This paper presents concepts related to automated experimentation, as controlled experiments, machine learning and software architectures for adaptation. However, this paper focuses on how architectural aspects that can contribute to support automated experimentation. A case study using an autonomous system is used to demonstrate the developed initial architecture framework. The contributions of this paper are threefold. First, it identifies software architecture qualities to support automated experimentation. Second, it develops an initial architecture framework that supports automated experiments and validates the framework with an autonomous mobile robot. Third, it identifies key research challenges that need to be addressed to support further development of automated experimentation.

  • 84.
    Mattos, David Issa
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    Dakkak, Anas
    Ericsson, Stockholm, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Experimentation for Business-to-Business Mission-Critical Systems2020Ingår i: ICSSP '20: Proceedings of the International Conference on Software and System Processes, Association for Computing Machinery (ACM), 2020, s. 95-104Konferensbidrag (Refereegranskat)
    Abstract [en]

    Continuous experimentation (CE) refers to a group of practices used by software companies to rapidly assess the usage, value and performance of deployed software using data collected from customers and the deployed system. Despite its increasing popularity in the development of web-facing applications, CE has not been discussed in the development process of business-to-business (B2B) mission-critical systems.

    We investigated in a case study the use of CE practices within several products, teams and areas inside Ericsson. By observing the CE practices of different teams, we were able to identify the key activities in four main areas and inductively derive an experimentation process, the HURRIER process, that addresses the deployment of experiments with customers in the B2B and with mission-critical systems. We illustrate this process with a case study in the development of a large mission-critical functionality in the Long Term Evolution (4G) product. In this case study, the HURRIER process is not only used to validate the value delivered by the solution but to increase the quality and the confidence from both the customers and the R&D organization in the deployed solution. Additionally, we discuss the challenges, opportunities and lessons learned from applying CE and the HURRIER process in B2B mission-critical systems.

  • 85.
    Mattos Issa, David
    et al.
    Chalmers University of Technology.
    Bosch, Jan
    Chalmers University of Technology.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Leveraging Business Transformation with Machine Learning Experiments2019Konferensbidrag (Refereegranskat)
  • 86.
    Mattos Issa, David
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Göteborg, 412 96, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Göteborg, 412 96, Sweden.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Challenges and Strategies for Undertaking Continuous Experimentation to Embedded Systems: Industry and Research Perspectives2018Ingår i: XP 2018: Agile Processes in Software Engineering and Extreme Programming, Springer, 2018, s. 277-292Konferensbidrag (Refereegranskat)
    Abstract [en]

    Context: Continuous experimentation is frequently used in web-facing companies and it is starting to gain the attention of embedded systems companies. However, embedded systems companies have different challenges and requirements to run experiments in their systems. Objective: This paper explores the challenges during the adoption of continuous experimentation in embedded systems from both industry practice and academic research. It presents strategies, guidelines, and solutions to overcome each of the identified challenges. Method: This research was conducted in two parts. The first part is a literature review with the aim to analyze the challenges in adopting continuous experimentation from the research perspective. The second part is a multiple case study based on interviews and workshop sessions with five companies to understand the challenges from the industry perspective and how they are working to overcome them. Results: This study found a set of twelve challenges divided into three areas; technical, business, and organizational challenges and strategies grouped into three categories, architecture, data handling and development processes. Conclusions: The set of identified challenges are presented with a set of strategies, guidelines, and solutions. To the knowledge of the authors, this paper is the first to provide an extensive list of challenges and strategies for continuous experimentation in embedded systems. Moreover, this research points out open challenges and the need for new tools and novel solutions for the further development of experimentation in embedded systems.

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  • 87.
    Mattos Issa, David
    et al.
    Chalmers University of Technology, Computer Science and Engineering, Hörselgången 4, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Computer Science and Engineering, Hörselgången 4, Gothenburg, Sweden.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Multi-armed bandits in the wild: Pitfalls and strategies in online experiments2019Ingår i: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 113, s. 68-81Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Delivering faster value to customers with online experimentation is an emerging practice in industry. Multi-Armed Bandit (MAB) based experiments have the potential to deliver even faster results with a better allocation of resources over traditional A/B experiments. However, the incorrect use of MAB-based experiments can lead to incorrect conclusions that can potentially hurt the company's business. The objective of this study is to understand the pitfalls and restrictions of using MABs in online experiments, as well as the strategies that are used to overcome them. This research uses a multiple case study method with eleven experts across five software companies and simulations to triangulate the data of some of the identified limitations. This study analyzes some limitations faced by companies using MAB and discusses strategies used to overcome them. The results are summarized into practitioners’ guidelines with criteria to select an appropriated experimental design. MAB algorithms have the potential to deliver even faster results with a better allocation of resources over traditional A/B experiments. However, potential mistakes can occur and hinder the potential benefits of such approach. Together with the provided guidelines, we aim for this paper to be used as reference material for practitioners during the design of an online experiment.

  • 88.
    Mattos Issa, David
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Dimitriev, Pavel
    Outreach, 1441 North 34th Street, Seattle, 98103, WA, United States.
    Fabijan, Aleksander
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Gothenburg, 412 96, Sweden.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    An Activity and Metric Model for Online Controlled Experiments2018Ingår i: PROFES 2018: Product-Focused Software Process Improvement, Springer, 2018, s. 182-198Konferensbidrag (Refereegranskat)
    Abstract [en]

    Accurate prioritization of efforts in product and services development is critical to the success of every company. Online controlled experiments, also known as A/B tests, enable software companies to establish causal relationships between changes in their systems and the movements in the metrics. By experimenting, product development can be directed towards identifying and delivering value. Previous research stresses the need for data-driven development and experimentation. However, the level of granularity in which existing models explain the experimentation process is neither sufficient, in terms of details, nor scalable, in terms of how to increase number and run different types of experiments, in an online setting. Based on a case study of multiple products running online controlled experiments at Microsoft, we provide an experimentation framework composed of two detailed experimentation models focused on two main aspects; the experimentation activities and the experimentation metrics. This work intends to provide guidelines to companies and practitioners on how to set and organize experimentation activities for running trustworthy online controlled experiments.

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  • 89.
    Mattos Issa, David
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Göteborg, 412 96, Sweden.
    Mårtensson, Erling
    Sony Mobile Communications, Nya Vattentornet, Lund, 221 88, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, Göteborg, 412 96, Sweden.
    Olsson Holmström, Helena
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Optimization Experiments in the Continuous Space the Limited Growth Optimistic Optimization Algorithm2018Ingår i: SSBSE 2018: Search-Based Software Engineering, Springer, 2018, s. 293-308Konferensbidrag (Refereegranskat)
    Abstract [en]

    Online controlled experiments are extensively used by web-facing companies to validate and optimize their systems, providing a competitive advantage in their business. As the number of experiments scale, companies aim to invest their experimentation resources in larger feature changes and leave the automated techniques to optimize smaller features. Optimization experiments in the continuous space are encompassed in the many-armed bandits class of problems. Although previous research provides algorithms for solving this class of problems, these algorithms were not implemented in real-world online experimentation problems and do not consider the application constraints, such as time to compute a solution, selection of a best arm and the estimation of the mean-reward function. This work discusses the online experiments in context of the many-armed bandits class of problems and provides three main contributions: (1) an algorithm modification to include online experiments constraints, (2) implementation of this algorithm in an industrial setting in collaboration with Sony Mobile, and (3) statistical evidence that supports the modification of the algorithm for online experiments scenarios. These contributions support the relevance of the LG-HOO algorithm in the context of optimization experiments and show how the algorithm can be used to support continuous optimization of online systems in stochastic scenarios.

  • 90.
    Moe, Nils Brede
    et al.
    SINTEF, NO-7465 Trondheim, Norway..
    Olsson, Helena Holmström
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap (DV).
    Dingsøyr, Torgeir
    SINTEF, Trondheim, NO-7465, Norway.
    Trends in Large-Scale Agile Development: A Summary of the 4th Workshop at XP20162016Ingår i: Proceedings of the XP2016 Scientific Workshops, ACM Digital Library, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Large projects are increasingly adopting agile development practices, and this raises new challenges for research and practice. The fourth workshop on large-scale agile development focused on the following topics: Distributed large-scale development, inter-team coordination, knowledge sharing, large-scale agile transformations, multidisciplinary work, and new ways-of-organizing for advancing agile practices.

  • 91.
    Munappy, A. R.
    et al.
    Chalmers University of Technology.
    Bosch, J.
    Chalmers University of Technology.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    On the Trade-off Between Robustness and Complexity in Data Pipelines2021Ingår i: Quality of Information and Communications Technology: 14th International Conference, QUATIC 2021, Algarve, Portugal, September 8–11, 2021, Proceedings / [ed] Ana C. R. Paiva, Ana Rosa Cavalli, Paula Ventura Martins, Ricardo Pérez-Castillo, Springer, 2021, s. 401-415Konferensbidrag (Refereegranskat)
    Abstract [en]

    Data pipelines play an important role throughout the data management process whether these are used for data analytics or machine learning. Data-driven organizations can make use of data pipelines for producing good quality data applications. Moreover, data pipelines ensure end-to-end velocity by automating the processes involved in extracting, transforming, combining, validating, and loading data for further analysis and visualization. However, the robustness of data pipelines is equally important since unhealthy data pipelines can add more noise to the input data. This paper identifies the essential elements for a robust data pipeline and analyses the trade-off between data pipeline robustness and complexity.

  • 92.
    Munappy, Aiswarya
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Arpteg, Anders
    Peltarion AB, Stockholm, Sweden.
    Brinne, Björn
    Peltarion AB, Stockholm, Sweden.
    Data Management Challenges for Deep Learning2019Ingår i: 201945th Euromicro Conference On Software Engineering And Advanced Applications (SEAA 2019) / [ed] Staron, M Capilla, R Skavhaug, A, IEEE, 2019, s. 140-147Konferensbidrag (Refereegranskat)
    Abstract [en]

    Deep learning is one of the most exciting and fast-growing techniques in Artificial Intelligence. The unique capacity of deep learning models to automatically learn patterns from the data differentiates it from other machine learning techniques. Deep learning is responsible for a significant number of recent breakthroughs in AI. However, deep learning models are highly dependent on the underlying data. So, consistency, accuracy, and completeness of data is essential for a deep learning model. Thus, data management principles and practices need to be adopted throughout the development process of deep learning models. The objective of this study is to identify and categorise data management challenges faced by practitioners in different stages of end-to-end development. In this paper, a case study approach is employed to explore the data management issues faced by practitioners across various domains when they use real-world data for training and deploying deep learning models. Our case study is intended to provide valuable insights to the deep learning community as well as for data scientists to guide discussion and future research in applied deep learning with real-world data.

  • 93.
    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 universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (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 solutions2022Ingår i: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 191, artikel-id 111359Artikel i tidskrift (Refereegranskat)
    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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  • 94.
    Munappy, Aiswarya Raj
    et al.
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Gothenburg, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Gothenburg, Sweden.
    Olsson, Helena Homström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Data Pipeline Management in Practice: Challenges and Opportunities2020Ingår i: Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020, Proceedings / [ed] Maurizio Morisio; Marco Torchiano; Andreas Jedlitschka, Springer, 2020, s. 168-184Konferensbidrag (Refereegranskat)
    Abstract [en]

    Data pipelines involve a complex chain of interconnected activities that starts with a data source and ends in a data sink. Data pipelines are important for data-driven organizations since a data pipeline can process data in multiple formats from distributed data sources with minimal human intervention, accelerate data life cycle activities, and enhance productivity in data-driven enterprises. However, there are challenges and opportunities in implementing data pipelines but practical industry experiences are seldom reported. The findings of this study are derived by conducting a qualitative multiple-case study and interviews with the representatives of three companies. The challenges include data quality issues, infrastructure maintenance problems, and organizational barriers. On the other hand, data pipelines are implemented to enable traceability, fault-tolerance, and reduce human errors through maximizing automation thereby producing high-quality data. Based on multiple-case study research with five use cases from three case companies, this paper identifies the key challenges and benefits associated with the implementation and use of data pipelines.

  • 95.
    Munappy, Aiswarya Raj
    et al.
    Chalmers University of Technology, Göteborg, Sweden.
    Mattos, David Issa
    Chalmers University of Technology, Göteborg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Göteborg, Sweden.
    Olsson, Helena Holmström
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dakkak, Anas
    Ericsson, Stockholm, Sweden.
    From Ad-Hoc Data Analytics to DataOps2020Ingår i: ICSSP '20: Proceedings of the International Conference on Software and System Processes, Association for Computing Machinery (ACM), 2020, s. 165-174Konferensbidrag (Refereegranskat)
    Abstract [en]

    The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.

    DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.

    This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage.

  • 96.
    Olsson, Helena H.
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology, Department of Computer Science and Engineering, Gothenburg, 41296, Sweden.
    What Got You Here Won’t Get You There: A multi-case study on the challenges in the transition from traditional towards continuous data practices in the embedded systems domain2023Ingår i: 1st International Conference on Software Product Management 2023, Gesellschaft für Informatik, 2023, s. 47-62Konferensbidrag (Refereegranskat)
    Abstract [en]

    For decades, product data has been collected and used for quality assurance, for post-deployment defect detection and for informing the next generation of products. Across industry domains, and with the online domain leading the way, companies have adopted experimentation and data driven practices such as A/B testing to evaluate product performance, customer behaviors and for determining what adds value to customers. However, with the rapid changes that new digital technologies bring, companies are moving towards continuous value delivery and monetization models in which they offer their products as-a-service or offer services to complement and extend their existing products. In this transition, the traditional way of post-deployment data collection and use is no longer sufficient. While companies realize this, they experience difficulties in making the changes they need to transition towards continuous practices and new ways of working with data. As a result, companies risk wasting development efforts on functionality that have little or no customer value and they lose out on the competitive advantages that come with insights derived from continuous collection and use of data. In this paper, we explore the challenges companies experience in the transition from traditional towards continuous practices and the implications this shift has on their ways of working with data.

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  • 97.
    Olsson, Helena Holmström
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    All data is equal or is some data more equal? On strategic data collection and use in the embedded systems domain2023Ingår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag (Refereegranskat)
    Abstract [en]

    Effective collection and use of data is key for companies across domains and it is only increasing in importance. For companies in the embedded systems domain, data constitutes the basis not only for quality assurance and diagnostics of their systems but also for new service development and innovation. For these companies, data is an enabler for continuous delivery of customer value and hence, a key asset for entirely new and recurring revenue streams. However, effective use of data requires careful collection of different kinds of data depending on the purpose and context for which it is intended to be used. In this paper, we identify the challenges that companies experience in their contemporary data practices and we outline the kinds of data that companies need to collect as they evolve through different maturity stages. In addition, we provide concrete guidance on the specific data to collect during each maturity stage.

  • 98.
    Olsson, Helena Holmström
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Data Driven Development: Challenges in Online, Embedded and On-Premise Software2019Ingår i: Product-Focused Software Process Improvement: 20th International Conference, PROFES 2019, Barcelona, Spain, November 27–29, 2019, Proceedings / [ed] Xavier Franch, Tomi Männistö, Silverio Martínez-Fernández, Springer, 2019, s. 515-527Konferensbidrag (Refereegranskat)
    Abstract [en]

    For more than a decade, data driven development has attracted attention as one of the most powerful means to improve effectiveness and ensure value delivery to customers. In online companies, controlled experimentation is the primary technique to measure how customers respond to variants of deployed software. In B2B companies, an interest for data driven development is rapidly emerging and experiments are run on selected instances of the system or as comparisons of previously computed data to ensure quality, improve configurations and explore new value propositions. Although the adoption of data driven development is challenging in general, it is especially so for embedded systems companies and for companies developing on-premise software solutions. Due to complex systems with hardware dependencies, safety-critical functionality and strict regulations, these companies have longer development cycles, less frequent deployments and limited access to data. In this paper, and based on multi-case study research, we explore the specific challenges that embedded systems companies and companies developing on-premise solutions experience when adopting data driven development practices. The contribution of the paper is two-fold. First, we provide empirical evidence in which we identify the key challenges that embedded systems and on-premise software solutions companies experience as they evolve through the process of adopting data driven development practices. Second, we define the key focus areas that these companies need to address for evolving their data driven development adoption process .

  • 99.
    Olsson, Helena Holmström
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Going digital: Disruption and transformation in software-intensive embedded systems ecosystems2020Ingår i: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 32, nr 6, artikel-id e2249Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Digitalization is transforming industry to an extent that we have only seen the beginnings of. Across domains, companies experience rapid changes to their existing practices due to new technologies and new entrants that current businesses. While digitalization brings endless opportunities, it comes with challenges that require companies to strategically engage with partners in their surrounding ecosystems. In this paper, we study how companies in the embedded systems domain experience the process of transitioning from product-based companies to businesses where software, data, and artificial intelligence (AI) play an increasingly important role. To manage this, these companies need to evolve their existing ecosystems while at the same time create new ecosystems around new technologies. This involves maintaining existing technologies such as mechanics and electronics while at the same time expanding these with software, data, and AI. We provide a strategic decision framework that helps software-intensive embedded systems companies to successfully navigate the digital transformation. We do this in two steps. First, we present three models that provide the technical content of the strategic decision framework. Second, we provide an overview of the strategic alternatives that incumbents and new entrants have available when existing technologies are commoditizing and new technologies are introduced.

  • 100.
    Olsson, Helena Holmström
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bosch, Jan
    Chalmers University of Technology,Dept. of Computer Science and Engineering,Gothenburg,Sweden.
    Living in a Pink Cloud or Fighting a Whack-a-Mole? On the Creation of Recurring Revenue Streams in the Embedded Systems Domain2022Ingår i: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2022Konferensbidrag (Refereegranskat)
    Abstract [en]

    For companies in the embedded systems domain, digitalization and digital technologies allow endless opportunities for new business models and continuous value delivery. While physical products still provide the core revenue, these are rapidly being complemented with offerings that allow for recurring revenue and that are based on software, data and artificial intelligence (AI). However, while new digital offerings allow for fundamentally new and recurring revenue streams and continuous value delivery to customers, the creation of these proves to be a challenging endeavour. In this paper, we study how companies explore ways to create new or additional value with the intention to complement their product portfolio with offerings that allow for recurring revenue. Based on multi-case study research, we identify the key challenges that companies in the embedded systems domain experience and we derive four organizational patterns that we see slow down innovation. Second, we present a framework outlining alternative types of offerings to customers. Third, we provide a value taxonomy in which we detail the different types of offerings and the value these provide to customers. For each value offering, we indicate whether this offering is (1) static or evolving, (2) bundled or unbundled, (3) free or monetized, and we provide examples from the case companies we studied.

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