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  • 1.
    Alkhabbas, Fahed
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    De Sanctis, Martina
    Gran Sasso Sci Inst, Comp Sci Dept, Laquila, Italy..
    Bucchiarone, Antonio
    Fdn Bruno Kessler, Trento, Italy..
    Cicchetti, Antonio
    Mälardalen Univ, IDT Dept, Västerås, Sweden..
    Spalazzese, Romina
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Iovino, Ludovico
    Gran Sasso Sci Inst, Comp Sci Dept, Laquila, Italy..
    ROUTE: A Framework for Customizable Smart Mobility Planners2022In: IEEE 19TH INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE (ICSA 2022), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 169-179Conference paper (Refereed)
    Abstract [en]

    Multimodal journey planners are used worldwide to support travelers in planning and executing their journeys. Generated travel plans usually involve local mobility service providers, consider some travelers' preferences, and provide travelers information about the routes' current status and expected delays. However, those planners cannot fully consider the special situations of individual cities when providing travel planning services. Specifically, authorities of different cities might define customizable regulations or constraints of movements in the cities (e.g., due to construction works or pandemics). Moreover, with the transformation of traditional cities into smart cities, travel planners could leverage advanced monitoring features. Finally, most planners do not consider relevant information impacting travel plans, for instance, information that might be provided by travelers (e.g., a crowded square) or by mobility service providers (e.g., changing the timetable of a bus). To address the aforementioned shortcomings, in this paper, we propose ROUTE, a framework for customizable smart mobility planners that better serve the needs of travelers, local authorities, and mobility service providers in the dynamic ecosystem of smart cities. ROUTE is composed of an architecture, a process, and a prototype developed to validate the feasibility of the framework. Experiments' results show that the framework scales well in both centralized and distributed deployment settings.

  • 2.
    Andersson, Andreas
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Stjernborg, Kevin
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Giftfri: En mobilapplikation som varnar för farliga kemikalier i kosmetika2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Chemicals in cosmetics are dangerous for both humans and the environment. Multiple studies and reports estimate that Sweden will not reach its climate goals, in large part because of the usage of chemicals. The law meant to protect humans and nature against chemicals is flawed since it is based on a state of knowledge that is flawed. Mobile applications which are meant to increase awareness of chemical usage are flawed in scope, functionality and credibility.

     

    The purpose of this study is to develop a mobile application which improves upon the flaws identified in existing mobile applications. The study uses design science research methodology, DSRM, to develop the artefact. DSRM is an iterative process with six steps where the development process is documented. The study is carried out with the company Consid AB which acts as stakeholders for the research project. The mobile application is evaluated in a descriptive method and analyzed by the authors of the study.

     

    The results of the study show that the functional flaws identified from existing applications are possible to improve and that the developed application highlights chemicals with the flawed state of knowledge in mind.

  • 3.
    Araya, Woldereta
    et al.
    Malmö University, Faculty of Technology and Society (TS).
    Hazem, Sarmad
    Malmö University, Faculty of Technology and Society (TS).
    Parameterstyrd projektering: En studie av parameterstyrd projektering utav en fackverkskomponent2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The endeavor for optimization and development in the construction industry is warranted where new solutions are highly sought for. The importance of communication and efficiency in a project planning phase is of highest importance. The objective of this study is to highlight the advantages and disadvantages of a parameter-driven modeling method, by using a Grasshopper software. To explore the possibilities of the software, a script will be created. The script will contain a parallel framework also known as Warren’s framework. In this study, a survey is sent for applicable practitioners in the construction industry where the purpose is to receive information about the industry's opinion about the subject. A parameter-driven modeling method refers to software such as Grasshopper and Dynamo. The software is node-based unlike traditional modeling methods based on manual line drawing. There is no fixed geometry in a parameter-driven modeling software. Therefore, in order to illustrate models, different visualization platforms like Rhino are needed. A considerable amount of time has been spent creating a script in Grasshopper where a user with limited experience will naturally face complications among the way. Many of the survey participants considered that the main con of parametric modeling is the amount of time allocated to creating a script. Thought when the script is completed, changing parameters to influence the model outcome is uncomplicated. Additionally, there is potential regarding time efficiency since the software eliminates all need to perform repetitive tasks. Parametric modeling is a flexible form of work where changes later in a stage are always available.

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  • 4.
    Bengtsson, Milo
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Pamp, Jesper
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Factors for Adopting and Implementing Accessibility as a Cornerstone in Software Development Processes in Organizations2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Due to the increasing prevalence of laws, standards, and ethical discussions about web accessibility, developing websites and apps that are usable to everyone—regardless of disability and impairment—is more important than ever. In spite of this, most of the web is still inaccessible and accessibility is commonly treated as an afterthought. The aim of this thesis is to investigate how to implement web accessibility as a cornerstone of software development processes, and more specifically what motivates accessibility adoption and how to implement it successfully.

    The main part of the research consists of an extensive analysis of the literature to identify common themes. Although legislation and financial concerns play a role in why organizations adopt accessibility, the most frequently cited factors are social and ethical aspects as well as reaching a wider audience. The success of accessibility implementation is largely dependent on how knowledge is created, maintained, and disseminated in organizations. Prioritizing it and integrating it as much as any other basic requirement is also a key to success. Moreover, WCAG 2.1 Level AA is the recommended accessibility standard and compliance level, as informed by a law review and insights from literature. In addition, semi-structured interviews and a workshop were conducted with participants across three projects of a Swedish IT company with the aim of applying the findings in a real-life context and bringing about change in the organization.

    Final suggestions were based on the thematic analysis, and adapted to the case company through the insights from interviews and the workshop, as well as process documentation and corporate policies. Although not generalizable to all organizations, the suggestions provide understanding of enterprises sharing the case company's characteristics. Furthermore, the thematic analysis and law review have relevance for all types of private organizations.

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  • 5.
    Bosch, Jan
    et al.
    Software Center and Chalmers University of Technology.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Software Center.
    Brinne, Bjorn
    Peltarion.
    Crnkovic, Ivica
    Chalmers Artificial Intelligence Research Center and Chalmers University of Technology.
    AI Engineering: Realizing the Potential of AI2022In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 39, no 6, p. 23-27Article in journal (Refereed)
    Abstract [en]

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

  • 6.
    Bosch, Jan
    et al.
    Chalmers University of Technology, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Crnkovic, Ivica
    Chalmers University of Technology, Sweden.
    Engineering AI Systems: A Research Agenda2021In: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems / [ed] Ashish Kumar Luhach; Atilla Elçi, IGI Global, 2021, p. 1-19Chapter in book (Refereed)
    Abstract [en]

    Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.

  • 7.
    Bosch, Jan
    et al.
    Chalmers University of Technology, Department of Computer Science & Engineering, Göteborg, Sweden .
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Crnkovic, Ivica
    Chalmers University of Technology, Department of Computer Science & Engineering, Göteborg, Sweden .
    It Takes Three to Tango: Requirement, Outcome/data, and AI Driven Development2018In: Proceedings of the International Workshop on Software-intensive Business: Start-ups, Ecosystems and Platforms (SiBW 2018), CEUR-WS.org , 2018, p. 177-192Conference paper (Refereed)
    Abstract [en]

    Today’s software-intensive organizations are experiencing a paradigm-shift with regards to how to develop software systems. With the increasing availability and access to data and with artificial intelligence (AI) and technologies such as machine learning and deep learning emerging, the traditional requirement driven approach to software development is becoming complemented with other approaches. In addition to having development teams executing on requirements specified by product management, the development of software systems is progressing towards a data driven practice where teams receive an outcome to realize and where design decisions are taken based on continuous collection and analysis of data. On top of this, and due to artificial intelligence components being introduced to more and more software systems, learning algorithms, automatically generated models and data is replacing code and the development process is no longer only a manual effort but instead a combination of human and automated processes. In this paper, and based on multi-case study research in embedded systems and online companies, we see that companies use different approaches to software development but that they often take a requirement driven approach even if they would benefit from one of the other two. Also, we see that picking the wrong approach results in a number of problems such as e.g. inefficiency and waste of development efforts. To help address these problems, we develop a holistic development framework and we provide guidelines on how to improve effectiveness in development. The contribution of this paper is two-fold. First, we identify that there are three distinct approaches to software development; (1) Requirement driven development, (2) Outcome/data driven development and (3) AI driven development and we outline the typical problems that companies experience when using the wrong approach for the wrong purpose. Second, we provide a holistic framework with guidelines for when to use what approach to software development. 

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  • 8.
    Bothén, Simon
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Font, Jose
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Nilsson, Patrik
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    An analysis and comparative user study on interactions in mobile virtual reality games2018In: FDG '18: Proceedings of the 13th International Conference on the Foundations of Digital Games, Association for Computing Machinery (ACM), 2018, article id 4Conference paper (Refereed)
    Abstract [en]

    Mobile Virtual Reality (MVR) makes Virtual Reality games more accessible to a broader audience. Interaction design guidelines and best practices for MVR experiences are available for developers. In this paper, we specifically explore interactions in MVR games, a particular subset of MVR experiences that is becoming popular. A set of MVR games is analyzed with a special focus on head gaze, categorizing and isolating their mechanics implemented with this common MVR technique. This analysis is the basis of a test application in the MVR interactions are implemented and later compared to a traditional game pad controller in three different challenges. A comparative user study has been carried out from the perspective of both gamers and non-gamers facing these challenges. Results show the preferences and performances of the players using all the interactions, highlighting an interesting generalized preference for MVR interactions over the traditional controller in some of the analyzed cases.

  • 9.
    Dakkak, Anas
    et al.
    Ericsson AB, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Controlled Continuous Deployment: A Case Study From The Telecommunications Domain2022In: Proceedings of the International Conference on Software and System Processes and International Conference on Global Software Engineering, Association for Computing Machinery (ACM), 2022, p. 24-33Conference paper (Refereed)
    Abstract [en]

    Continuous deployment has become a widely used practice in web-based software applications. Deploying a new software version to production is a seamless automated process executed thousands of times per day. Continuous deployment reduces the time between a code commit and that commit is active in production. While continuous deployment promises many advantages to software development organizations, the adoption of continuous deployment in the software-intensive embedded systems industry is limited. Several empirical studies have highlighted the challenges associated with software-intensive embedded systems. However, very few studies, if any at all, have attempted to provide a practical approach to realize continuous deployment to these systems. This paper proposes a Controlled Continuous Deployment (CCD) approach, which considers the constraints software-intensive embedded systems have, such as high reliability and availability requirements, limited possibility for rollback after deployment, and the high volume of in-service systems in the market. We derived the approach by conducting a case study at Ericsson AB, focusing on three Radio Access Networks (RAN) technologies embedded software used in 3G, 4G, and 5G mobile networks.  

  • 10.
    Dakkak, Anas
    et al.
    Ericsson AB, Stockholm, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    DevServOps: DevOps For Product-Oriented Product Service Systems2023In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    Abstract [en]

    Companies producing software-intensive products do not only offer products to customers but Product Service Systems (PSS), a combination of the products and services that address customers’ needs. Further, product-related services are key in ensuring customer satisfaction as the service organization represents the company’s interface toward its customers, who operate and use the products. Therefore, while DevOps has been widely adopted in companies developing web-based applications aiming to streamline the Development and Operations activities, the projecting of DevOps as applied in web-based applications to PSS is difficult without considering the role of services. Therefore, based on a two years participant observation case study conducted at a multinational telecommunications systems provider, we propose a new and novel approach called Development-Services-Operations (DevServOps) which incorporates services as a key player facilitating an end-to-end software flow toward customers in one direction and feedback toward developers in the other direction.

  • 11.
    Dakkak, Anas
    et al.
    Ericsson AB,Stockholm,Sweden.
    Bosch, Jan
    Chalmers University of Technology,Göteborg,Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    The Role Of Post-Release Software Traceability in Release Engineering: A Software-Intensive Embedded Systems Case Study From The Telecommunications Domain2022In: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper (Refereed)
    Abstract [en]

    Modern release engineering practices such as continuous integration and delivery have allowed software development companies to transition from a long release cycle to a shorter one. The shorter release cycle has led to more software releases available to customers. At the same time, companies developing high-volume software-intensive embedded systems often deliver patch releases and maintenance releases on top of major and minor releases to customers who pick and choose what releases apply to them and decide when to upgrade the system, if to upgrade at all. While release engineering has been studied before in web-based, desktop-based, and embedded software, the focus has been on pre-release activities. Few studies have investigated what happens after the release, particularly the role of tracing software from release to deployment in high-volume software-intensive embedded systems. To address this gap, we conducted a qualitative case study at a multi-national telecommunications systems provider focusing on Radio Access Network (RAN) software. RAN software is a complex and large-scale embedded software used in mobile networks Base Stations (BS), providing software functionality for RAN mobile technologies ranging from 2G to 5G. Our study shed light on post-release software traceability and how it is used in the release engineering process.

  • 12.
    Dakkak, Anas
    et al.
    Ericsson AB, Stockholm, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Towards AIOps enabled services in continuously evolving software-intensive embedded systems2023In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481Article in journal (Refereed)
    Abstract [en]

    Continuous deployment has been practiced for many years by companies developing web- and cloud-based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time-consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software-intensive embedded systems often provide customer product-related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in-depth understanding of the impact of continuous deployment on product-related services provided by software-intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software-intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product-related services become blurry with continuous deployment. In addition, product-related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software-intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.

  • 13.
    Dakkak, Anas
    et al.
    Ericsson AB, Torshamnsgatan 21, Stockholm, 164 83, Sweden.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Chalmersplatsen 1, Gothenburg, 412 96, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Issa Mattos, David
    Department of Computer Science and Engineering, Chalmers University of Technology, Chalmersplatsen 1, Gothenburg, 412 96, Sweden.
    Continuous deployment in software-intensive system-of-systems2023In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 159, p. 107200-107200, article id 107200Article in journal (Refereed)
    Abstract [en]

    Context:While continuous deployment is popular among web-based software development organizations, adopting continuous deployment in software-intensive system-of-systems is more challenging. On top of the challenges arising from deploying software to a single software-intensive embedded system, software-intensive system-of-systems (SiSoS) add a layer of complexity as new software undergoes an extensive field validation applied to individual components of the SiSoS, as well as the overall SiSoS, to ensure that both legacy and new functionalities are working as desired.

    Objectives:This paper aims to study how SiSoS transitions to continuous deployment by exploring how continuous deployment impacts field testing and validation activities, how continuous deployment can be practiced in SiSoS, and to identify the success factors that companies need to consider when transitioning to continuous deployment.

    Method:We conducted a case study at Ericsson AB focusing on the embedded software of the Third Generation Radio Access Network (3G RAN). The 3G RAN consists of two large-scale software-intensive embedded systems, representing a simple SiSoS composed of two systems. 3G RAN software was the first to transition to continuous deployment and is used as a reference case for other products within Ericsson AB.

    Results:Software deployment, in addition to field testing and validation, have transitioned from being a discrete activity performed at the end of software development to a continuous process performed in parallel to software development. Further, our study reveals an orchestrating approach for software deployment, which allows pre/post validation of legacy behavior and new features in a shorter release and deployment cadence. Furthermore, we identified the essential success factors that organizations should consider when transitioning to continuous deployment.

    Conclusion:Transition to continuous deployment, in addition to field testing and validation, shall be considered and planned carefully. In this paper, we provide a set of success factors and orchestration technique that helps organization when transitioning to continuous deployment in the software-intensive embedded system-of-systems context.

  • 14.
    Dakkak, Anas
    et al.
    Ericsson AB, Stockholm, Sweden..
    Munappy, Aiswarya Raj
    Chalmers Univ Technol, Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Customer Support In The Era of Continuous Deployment: A Software-Intensive Embedded Systems Case Study2022In: 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022) / [ed] Leong, HV Sarvestani, SS Teranishi, Y Cuzzocrea, A Kashiwazaki, H Towey, D Yang, JJ Shahriar, H, Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 914-923Conference paper (Refereed)
    Abstract [en]

    Supporting customers after they acquire the product is essential for companies producing and selling software-intensive embedded systems products. Generally, customer support is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While continuous deployment advocates for closer cooperation between the ones operating the software and the ones developing it, the means of such collaboration in general and the role of customer support, in particular, has not been addressed in the context of software-intensive embedded systems. Therefore, to better understand the impact that continuous deployment has on customer support and the role customer support should play in this context, we conducted a case study at a multinational company developing and selling telecommunications networks infrastructure. We focused on the 4th and 5th Generation (4G and 5G) Radio Access Networks (RAN) products, which can be considered a high volume product as they cover more than 80% of the world's population. Our study reveals that customer support needs to transition from a transaction-based and passive function triggered by customer support requests, to take an active role characterized by being proactive and preemptive to cope with the shorter operational time of a software version introduced by continuous deployment. In addition, customer support plays an essential role in making the feedback actionable by aggregating and consolidating feedback data to the R&D organization.

  • 15.
    Dakkak, Anas
    et al.
    Ericsson AB,Stockholm,Sweden.
    Zhang, Hongyi
    Chalmers University of Technology, Gothenburg, Sweden.
    Mattos, David Issa
    Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Towards Continuous Data Collection from In-service Products: Exploring the Relation Between Data Dimensions and Collection Challenges2021In: 2021 28th Asia-Pacific Software Engineering Conference (APSEC), IEEE, 2021Conference paper (Refereed)
    Abstract [en]

    Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While there is no shortage of possible use cases leveraging data from in-service products, software-intensive embedded systems companies struggle to continuously collect data from their in-service products. Often, data collection is done in an ad-hoc way and targeting specific use cases or needs. Besides, few studies have investigated data collection challenges in relation to the data dimensions, which are the minimum set of quantifiable data aspects that can define software-intensive embedded product data from a collection point of view. To help address data collection challenges, and to provide companies with guidance on how to improve this process, we conducted a case study at a large multinational telecommunications supplier focusing on data characteristics and collection challenges from the Radio Access Networks (RAN) products. We further investigated the relations of these challenges to the data dimensions to increase our understanding of how data dominions contribute to the challenges.

  • 16.
    Dzhusupova, Rimma
    et al.
    Electrical, Instrumentation, Control & Safety Systems McDermott The Hague The Netherlands.
    Banotra, Richa
    Instrumentation, Control & Safety Systems McDermott The Hague The Netherlands.
    Bosch, Jan
    Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Using artificial intelligence to find design errors in the engineering drawings2023In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 35, no 12Article in journal (Refereed)
    Abstract [en]

    Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project's overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry. 

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  • 17.
    Dzhusupova, Rimma
    et al.
    Eindhoven Univ Technol, Math & Comp Sci, Eindhoven, Netherlands..
    Bosch, Jan
    Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Choosing the right path for AI integration in engineering companies: A strategic guide2024In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 210, article id 111945Article in journal (Refereed)
    Abstract [en]

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

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  • 18.
    Eklund, Ulrik
    et al.
    Malmö högskola, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Berger, Christian
    2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP): A Comparative Case Study2017In: 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP), IEEE, 2017, p. 173-182Conference paper (Refereed)
    Abstract [en]

    Agile software development principles enable companies to successfully and quickly deliver software by meeting their customers' expectations while focusing on high quality. Many companies working with pure software systems have adopted these principles, but implementing them in companies dealing with non-pure software products is challenging. We identified a set of goals and practices to support large-scale agile development in companies that develop software-intense mechatronic systems. We used an inductive approach based on empirical data collected during a longitudinal study with six companies in the Nordic region. The data collection took place over two years through focus group workshops, individual on-site interviews, and complementary surveys. The primary benefit of large-scale agile development is improved quality, enabled by practices that support regular or continuous integration between teams delivering software, hardware, and mechanics. In this regard, the most beneficial integration cycle for deliveries is every four weeks; while continuous integration on a daily basis would favor software teams, other disciplines does not seem to benefit from faster integration cycles. We identified 108 goals and development practices supporting agile principles among the companies, most of them concerned with integration; therefrom, 26 agile practices are unique to the mechatronics domain to support adopting agile beyond pure software development teams. 16 of these practices are considered as key enablers, confirmed by our control cases.

  • 19.
    Eklund, Ulrik
    et al.
    Malmö högskola, Faculty of Technology and Society (TS), Department of Computer Science (DV).
    Olsson Holmström, Helena
    Malmö högskola, Faculty of Technology and Society (TS), Department of Computer Science (DV).
    Strøm, Niels Jørgen
    Grundfos A/S, DK-8850, Bjerringbro, Denmark.
    Industrial Challenges of Scaling Agile in Mass-Produced Embedded Systems2014In: Agile Methods. Large-Scale Development, Refactoring, Testing, and Estimation: XP 2014 International Workshops, Rome, Italy, May 26-30, 2014, Revised Selected Papers, Springer, 2014, p. 30-42Conference paper (Refereed)
    Abstract [en]

    When individual teams in mechatronic organizations attempt to adopt agile software practices, these practices tend to only affect mod- ules or sub-systems. The short iterations on team level do not lead to short lead-times in launching new or updated products since the overall R&D approach on an organization level is still governed by an overall stage gate or single cycle V-model. This paper identifies challenges for future research on how to combine the predictability and planning desired of mechanical manufacturing with the dynamic capabilities of modern agile software development. Scaling agile in this context requires an expansion in two dimensions: First, scal- ing the number of involved teams. Second, traversing necessary systems engineering activities in each sprint due to the co-dependency of software and hardware development.

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  • 20.
    Fabijan, Aleksander
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Dmitriev, Pavel
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Bosch, Jan
    Vermeer, Lukas
    Lewis, Dylan
    Three Key Checklists and Remedies for Trustworthy Analysis of Online Controlled Experiments at Scale2019In: 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2019), IEEE, 2019, p. 1-10Conference paper (Refereed)
    Abstract [en]

    Online Controlled Experiments (OCEs) are transforming the decision-making process of data-driven companies into an experimental laboratory. Despite their great power in identifying what customers actually value, experimentation is very sensitive to data loss, skipped checks, wrong designs, and many other 'hiccups' in the analysis process. For this purpose, experiment analysis has traditionally been done by experienced data analysts and scientists that closely monitored experiments throughout their lifecycle. Depending solely on scarce experts, however, is neither scalable nor bulletproof. To democratize experimentation, analysis should be streamlined and meticulously performed by engineers, managers, or others responsible for the development of a product. In this paper, based on synthesized experience of companies that run thousands of OCEs per year, we examined how experts inspect online experiments. We reveal that most of the experiment analysis happens before OCEs are even started, and we summarize the key analysis steps in three checklists. The value of the checklists is threefold. First, they can increase the accuracy of experiment setup and decision-making process. Second, checklists can enable novice data scientists and software engineers to become more autonomous in setting-up and analyzing experiments. Finally, they can serve as a base to develop trustworthy platforms and tools for OCE set-up and analysis.

  • 21.
    Fabijan, Aleksander
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Dmitriev, Pavel
    Olsson Holmström, Helena
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Bosch, Jan
    The Online Controlled Experiment Lifecycle2020In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 37, no 2, p. 60-67Article in journal (Refereed)
    Abstract [en]

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

  • 22.
    Figalist, Iris
    et al.
    Siemens AG, Corp Technol, Munich, Germany..
    Dieffenbacher, Marco
    FAU Erlangen Nuremberg, Inst Informat Syst, Erlangen, Germany..
    Eigner, Isabella
    FAU Erlangen Nuremberg, Inst Informat Syst, Erlangen, Germany..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Elsner, Christoph
    Siemens AG, Corp Technol, Erlangen, Germany..
    Mining Customer Satisfaction on B2B Online Platforms using Service Quality and Web Usage Metrics2020In: 2020 27TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2020), IEEE, 2020, p. 435-444Conference paper (Refereed)
    Abstract [en]

    In order to distinguish themselves from their competitors, software service providers constantly try to assess and improve customer satisfaction. However, measuring customer satisfaction in a continuous way is often time and cost intensive, or requires effort on the customer side. Especially in B2B contexts, a continuous assessment of customer satisfaction is difficult to achieve due to potential restrictions and complex provider-customer-end user setups. While concepts such as web usage mining enable software providers to get a deep understanding of how their products are used, its application to quantitatively measure customer satisfaction has not yet been studied in greater detail. For that reason, our study aims at combining existing knowledge on customer satisfaction, web usage mining, and B2B service characteristics to derive a model that enables an automated calculation of quantitative customer satisfaction scores. We apply web usage mining to validate these scores and to compare the usage behavior of satisfied and dissatisfied customers. This approach is based on domain-specific service quality and web usage metrics and is, therefore, suitable for continuous measurements without requiring active customer participation. The applicability of the model is validated by instantiating it in a real-world B2B online platform.

  • 23.
    Figalist, Iris
    et al.
    Corporate Technology, Siemens AG, 81739, Munich, Germany.
    Elsner, Christoph
    Corporate Technology, Siemens AG, 81739, Munich, Germany.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Göteborg, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions2020In: 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, p. 217-233Conference paper (Refereed)
    Abstract [en]

    Nowadays, machine learning (ML) is an integral component in a wide range of areas, including software analytics (SA) and business intelligence (BI). As a result, the interest in custom ML-based software analytics and business intelligence solutions is rising. In practice, however, such solutions often get stuck in a prototypical stage because setting up an infrastructure for deployment and maintenance is considered complex and time-consuming. For this reason, we aim at structuring the entire process and making it more transparent by deriving an end-to-end framework from existing literature for building and deploying ML-based software analytics and business intelligence solutions. The framework is structured in three iterative cycles representing different stages in a model’s lifecycle: prototyping, deployment, update. As a result, the framework specifically supports the transitions between these stages while also covering all important activities from data collection to retraining deployed ML models. To validate the applicability of the framework in practice, we compare it to and apply it in a real-world ML-based SA/BI solution.

  • 24.
    Figalist, Iris
    et al.
    Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany..
    Elsner, Christoph
    Siemens Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Breaking the vicious circle: A case study on why AI for software analytics and business intelligence does not take off in practice2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 184, article id 111135Article in journal (Refereed)
    Abstract [en]

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

  • 25.
    Figalist, Iris
    et al.
    Siemens Corporate Technology, Munich, Germany.
    Elsner, Christoph
    Siemens Corporate Technology, Munich, Germany.
    Bosch, Jan
    Chalmers.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Breaking the Vicious Circle: Why AI for software analytics and business intelligence does not take off in practice2020In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2020, p. 5-12Conference paper (Refereed)
    Abstract [en]

    In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often gets stuck in a prototypical stage and the results are rarely used to make decisions based on data. To understand the underlying root causes of this phenomenon, we conduct both an explanatory case study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical analytics to continuous and productively usable software analytics and business intelligence based on AI.

  • 26. Figalist, Iris
    et al.
    Elsner, Christoph
    Bosch, Jan
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Business as Unusual: A Model for Continuous Real-time Business Insights Based on Low Level Metrics2019In: 2019 45th Euromicro Conference On Software Engineering And Advanced Applications (SEAA 2019) / [ed] Staron, M Capilla, R Skavhaug, A, IEEE, 2019, p. 66-73Conference paper (Refereed)
    Abstract [en]

    A wide variety of tools to monitor and track software systems, such as websites or smartphone applications, during runtime already exists. However, their aggregated results are often not sufficient to answer questions on a product management level since these questions address several levels of complexity and abstractions, and tend to be formulated on a rather high level, for instance concerning the efficiency of their website structure for their users. A straightforward mapping between low level metrics and high level insights is typically not possible. This causes a gap that makes it challenging to continuously provide quantitative high-level insights in real-time. In order to address this challenge, we conducted a study within three distinct platforms and products, and propose a model based on our results. After defining a case for each of the independent platforms and products, we implemented a process to measure high level insights using low level metrics for each of these cases. Next, we compared the procedures and steps that were taken in each of the cases and derived a model that describes a generic approach how to utilize and process data in order to gain higher level insights. Our model structures the steps from data to knowledge over different levels of complexity and abstraction, namely operational, tactical, and strategic. Thereby, the knowledge acquired in each phase serves as input in the next phase which increases the measurable level of complexity with each iteration. Since the steps in our model are specifically arranged as a pipeline, it enables practitioners to automate a continuous and quantitative measurement of high level insights in real-time.

  • 27.
    Figalist, Iris
    et al.
    Corporate Technology, Siemens AG, 81739, Munich, Germany.
    Elsner, Christoph
    Corporate Technology, Siemens AG, 81739, Munich, Germany.
    Bosch, Jan
    Department of Computer Science and Engineering, Chalmers University of Technology, Hörselgången 11, 412 96, Göteborg, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Customer Churn Prediction in B2B Contexts2019In: Software Business: 10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019, Proceedings / [ed] Sami Hyrynsalmi, Mari Suoranta, Anh Nguyen-Duc, Pasi Tyrväinen, Pekka Abrahamsson, Springer, 2019, p. 378-386Conference paper (Refereed)
    Abstract [en]

    While business-to-customer (B2C) companies, in the telecom sector for instance, have been making use of customer churn prediction for many years, churn prediction in the business-to-business (B2B) domain receives much less attention in existing literature. Nevertheless, B2B-specific characteristics, such as a lower number of customers with much higher transactional values, indicate the importance of identifying potentially churning customers. To achieve this, we implemented a prediction model for customer churn within a B2B software product and derived a model based on the results. For one, we present an approach that enables the mapping of customer- and end-user-data based on “customer phases” which allows the prediction model to take all critical influencing factors into consideration. In addition to that, we introduce a B2B customer churn prediction process based on the proposed data mapping.

  • 28.
    Figalist, Iris
    et al.
    Siemens Corporate Technology, Germany.
    Elsner, Christoph
    Siemens Corporate Technology, Germany.
    Bosch, Jan
    Chalmers University of Technology.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Fast and curious: A model for building efficient monitoring- and decision-making frameworks based on quantitative data2021In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 132, article id 106458Article in journal (Refereed)
    Abstract [en]

    Context: Nowadays, the hype around artificial intelligence is at its absolute peak. Large amounts of data are collected every second of the day and a variety of tools exists to enable easy analysis of data. In practice, however, making meaningful use of it is way more challenging. For instance, affected stakeholders often struggle to specify their information needs and to interpret the results of such analyses. Objective: In this study we investigate how to enable continuous monitoring of information needs, and the generation of knowledge and insights for various stakeholders involved in the lifecycle of software-intensive products. The overarching goal is to support their decision making by providing relevant insights related to their area of responsibility. Methods: We implement multiple monitoringand decision-making frameworks for six individual, real-world cases selected from three different platforms and covering four types of stakeholders. We compare the individual procedures to derive a generic process for instantiating such frameworks as well as a model to scale it up for multiple stakeholders. Results: For one, we discovered that information needs of stakeholders are often related to a limited subset of data sources and should be specified in stages. For another, stakeholders often benefit from sharing and reusing existing components among themselves in later phases. Specifically, we identify three types of reuse: (1) Data and knowledge, (2) tools and methods, and (3) concepts. As a result, key aspects of our model are iterative feedback and specification cycles as well as the reuse of appropriate components to speed up the instantiation process and maximize the efficiency of the model. Conclusion: Our results indicate that knowledge and insights can be generated much faster and stakeholders feel the benefits of the analysis very early on by iteratively specifying information needs and by systematically sharing and reusing knowledge, tools and concepts.

  • 29.
    Hellstrand, Elliott
    et al.
    Malmö University, Faculty of Technology and Society (TS).
    Tu, Jacky
    Malmö University, Faculty of Technology and Society (TS).
    Mobile-first? - En Utredning av det Moderna Webbutvecklingsfältet2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Billions of people around the world use their mobile phone to surf on the web, and in recentdecades, we have seen new web technologies that facilitate the development of mobile webapplications. The purpose of this study is to investigate whether mobile-first will be thestandard for the future and how the ecosystem for web development will be affected by this.Mobile-first means that you prioritize the mobile platform first, compared with the traditio-nal desktop platform. In addition to this, we also want to investigate multi-platform toolssuch as progressive web applications and hybrid applications, but also how responsive webdesign affects web development. We will conduct two literature studies and an online surveywill be sent out to answer our research questions. Based on our selected methods, the resultsindicate that the mobile-first would be the way one chooses to prioritize for the current si-tuation. The reason is that mobile phone users have increased drastically in recent decadesdue to the lower prices of mobile phones, and internet telecommunications that have attrac-ted a larger market of users. In addition, it turns out that the web technologies between hy-brid applications and progressive web applications are still unexplored for most developers.This is due, for example, to a lack of interest in developing or that they are not compatiblewith the business’ system/product. The responsive web design was shown to be the one thathas had the most impact on web development, and is the concept that is the most famili-ar to developers. The definition of mobile-first in the literature and the online survey lar-gely agreed with each other, however, the literature provided a more well-defined answer.

  • 30.
    Hyrynsalmi, Sami
    et al.
    LUT University,Dept. Software Engineering,Lahti,Finland.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 Companies2023In: 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    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.

  • 31.
    Hyrynsalmi, Sami
    et al.
    LUT University, Mukkulankatu 19, 15210, Lahti, Finland.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 Economy2022In: 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, p. 141-148Conference paper (Refereed)
    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.

  • 32.
    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 University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    The HURRIER process for experimentation in business-to-business mission-critical systems2023In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 35, no 5, article id e2390Article in journal (Refereed)
    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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  • 33.
    John, Meenu Mary
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Bosch, J.
    Chalmers University of Technology.
    Towards MLOps: A Framework and Maturity Model2021In: Proceedings - 2021 47th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2021, IEEE, 2021, p. 334-341Conference paper (Refereed)
    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. 

  • 34.
    Khorashadizadeh, Saeed
    et al.
    Univ Teknol Malaysia, Fac Comp, Skudai, Malaysia..
    Ikuesan, Adeyemi Richard
    Community Coll Qatar, Sch Informat Technol, Dept Cybersecur & Networking, Doha, Qatar..
    Kebande, Victor R.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Generic 5G Infrastructure for IoT Ecosystem2020In: Emerging Trends in Intelligent Computing and Informatics: Data Science, Intelligent Information Systems and Smart Computing / [ed] Saeed, F Mohammed, F Gazem, N, Springer, 2020, p. 451-462Conference paper (Refereed)
    Abstract [en]

    While the Internet of Things (IoT) is still gaining rapid adoption in an upward trajectory means across many smart areas in recent years, still, there is a need to develop a scalable ecosystem that is able to support future IoT implementations, given the heterogeneity and increased information flow among IoT devices. The lack of effective interoperability, availability, reliability, and performance in IoT are a few challenges that hinder the effectiveness of IoT deployment and communication, and this has acted as a stumbling block for the optimisation of IoT platforms. That notwithstanding, the advent of the Fifth Generation (5G) networks, has seen a significant shift on how the IoT-paradigm operates. This article introduces a discussion on the generic 5G infrastructure for IoT environment that can support future deployments. The article begins by conducting a technical review of the evolution of the First generation (1G) through 5G cellular networks. Thereafter, an illustration of the Fourth Generation (4G) architecture, stating its features as a precedent that shows to what extent 5G network may thrive or support IoT ecosystems, is given. Furthermore, the paper explores both the architectural requirements and futuristic vision of 5G infrastructure in the perspective of IoT applications. Most importantly, the paper has also explored the IoT market share and forecast on growth and how it affects different industrial sectors. The authors believe that the conclusions that have been made in this paper will act as a pacesetter and give a direction worth exploring once the 5G infrastructure for IoT ecosystems is implemented.

  • 35. Klonowska, Kamilla
    et al.
    Lundberg, Lars
    Lennerstad, Håkan
    The maximum gain of increasing the number of preemptions in multiprocessor scheduling2009In: Acta Informatica, ISSN 0001-5903, E-ISSN 1432-0525, Vol. 46, no 4, p. 285-295Article in journal (Refereed)
    Abstract [en]

    We consider the optimal makespan C(P, m, i) of an arbitrary set P of independent jobs scheduled with i preemptions on a multiprocessor with m identical processors. We compare the ratio for such makespans for i and j preemptions, respectively, where i < j. This ratio depends on P, but we are interested in the P that maximizes this ratio, i. e. we calculate a formula for the worst case ratio G(m, i, j) defined as G(m, i, j) = max C(P, m, i)/C(P, m, j), where the maximum is taken over all sets P of independent jobs.

  • 36.
    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 University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 Questions2021In: Software Engineering Notes: an Informal Newsletter of The Specia, ISSN 0163-5948, E-ISSN 1943-5843, Vol. 46, no 1, p. 25-27Article in journal (Other academic)
    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.  

     

  • 37.
    Linåker, Johan
    et al.
    Software Engineering Research Group, Computer Science, Lund University.
    Munir, Hussan
    Software Engineering Research Group, Computer Science, Lund University.
    Runeson, Per
    Software Engineering Research Group, Computer Science, Lund University.
    Regnell, Björn
    Software Engineering Research Group, Computer Science, Lund University.
    Schrewelius, Claes
    Software Engineering Research Group, Computer Science, Lund University.
    A Survey on the Perception of Innovation in a Large Product-Focused Software Organization2015In: Software Business - 6th International Conference, ICSOB 2015, Braga, Portugal, June 10-12, 2015, Proceedings, Springer, 2015, Vol. 210, p. 66-80Conference paper (Refereed)
    Abstract [en]

    Context. Innovation is promoted in companies to help them stay competitive. Four types of innovation are defined: product, process, business, and organizational. Objective. We want to understand the perception of the innovation concept in industry, and particularly how the innovation types relate to each other. Method. We launched a survey at a branch of a multi-national corporation. Results. From a qualitative analysis of the 229 responses, we see that the understanding of the innovation concept is somewhat narrow, and mostly related to product innovation. A majority of respondents indicate that product innovation triggers process, business, and organizational innovation, rather than vice versa. However, there is a complex interdependency between the types. We also identify challenges related to each of the types. Conclusion. Increasing awareness and knowledge of different types of innovation, may improve the innovation. Further, they cannot be handled one by one, but in their interdependent relations. 

  • 38.
    Liu, Yuchu
    et al.
    Volvo Cars, Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Lantz, Jonn
    Volvo Cars, Gothenburg, Sweden..
    An architecture for enabling A/B experiments in automotive embedded software2021In: 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, p. 992-997Conference paper (Refereed)
    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.

  • 39.
    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 University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 software2021In: 2021 47TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2021) / [ed] Baldassarre, MT Scanniello, G Skavhaug, A, IEEE, 2021, p. 300-307Conference paper (Refereed)
    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.

  • 40. Lwakatare, Lucy Ellen
    et al.
    Raj, Aiswarya
    Crnkovic, Ivica
    Bosch, Jan
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions2020In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 127, article id 106368Article, review/survey (Refereed)
    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.

  • 41.
    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 University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    ACE: Easy Deployment of Field Optimization Experiments2019In: SOFTWARE ARCHITECTURE, ECSA 2019 / [ed] Bures, T Duchien, L Inverardi, P, Springer, 2019, p. 264-279Conference paper (Refereed)
    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.

  • 42.
    Mattos, David Issa
    et al.
    Chalmers University of Technology, Gothenburg, Sweden.
    Bosch, Jan
    Chalmers University of Technology, Gothenburg, Sweden.
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Korshani, Aita Maryam
    Volvo Cars, Gothenburg, Sweden.
    Lantz, John
    Volvo Cars, Gothenburg, Sweden.
    Automotive A/B testing: Challenges and Lessons Learned from Practice2020In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), IEEE, 2020, p. 101-109Conference paper (Refereed)
    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.

  • 43.
    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 University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Experimentation for Business-to-Business Mission-Critical Systems2020In: ICSSP '20: Proceedings of the International Conference on Software and System Processes, Association for Computing Machinery (ACM), 2020, p. 95-104Conference paper (Refereed)
    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.

  • 44.
    Moe, Nils Brede
    et al.
    SINTEF, NO-7465 Trondheim, Norway..
    Olsson, Helena Holmström
    Malmö högskola, Faculty of Technology and Society (TS), Department of Computer Science (DV).
    Dingsoyr, Torgeir
    Trends in Large-Scale Agile Development: A Summary of the 4th Workshop at XP20162016In: Proceedings of the XP2016 Scientific Workshops, ACM Digital Library, 2016Conference paper (Refereed)
    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.

  • 45.
    Munappy, Aiswarya Raj
    et al.
    Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..
    Bosch, Jan
    Chalmers Univ Technol, Dept Comp Sci & Engn, Horselgangen 11, S-41296 Gothenburg, Sweden..
    Olsson, Helena Holmström
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Arpteg, Anders
    Peltar operat AI platform, Hollandargatan 17, S-11160 Stockholm, Sweden..
    Brinne, Bjoern
    Peltar operat AI platform, Hollandargatan 17, S-11160 Stockholm, Sweden..
    Data management for production quality deep learning models: Challenges and solutions2022In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 191, article id 111359Article in journal (Refereed)
    Abstract [en]

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

    Download full text (pdf)
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  • 46.
    Måbrink, Alexander
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Möller, André
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Javascript code smells från en utvecklares perspektiv2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Software development can be a difficult and time consuming task. In addition, producing good code is even more difficult. Poor design and implementation choices in software code can result in an end product that is both difficult to understand and difficult to maintain. A collective name for implementation and design choices that is considered to have a negative impact or indicate something negative in software code is Code smells. In this study, we identify 34 unique code smells through a systematic literature study. The results are then ranked and validated with interviews with people who work or have worked with Javascript in a professional environment at some point during the past five years. The end result is a ranked list of 32 code smells that are applicable to Javascript. The result shows that the five highest ranked code smells are Variable name conflict in closures, Depth, Argument Type Mismatch, Duplicated code and Excessive global Variables.

    Download full text (pdf)
    fulltext
  • 47.
    Olsson, Helena Holmström
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 domain2023In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper (Refereed)
    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.

  • 48.
    Olsson, Helena Holmström
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 Software2019In: 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, p. 515-527Conference paper (Refereed)
    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 .

  • 49.
    Olsson, Helena Holmström
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Bosch, Jan
    Going digital: Disruption and transformation in software-intensive embedded systems ecosystems2020In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 32, no 6, article id e2249Article in journal (Refereed)
    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.

  • 50.
    Olsson, Helena Holmström
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (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 Domain2022In: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper (Refereed)
    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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