Obtaining labels for supervised learning is time-consuming, and practitioners seek to minimize manual labeling. Semi-supervised learning allows practitioners to eliminate manual labeling by including unlabeled data in the training process. With many deep semi-supervised algorithms and applications available, practitioners need guidelines to select the optimal labeling algorithm for their problem. The performance of new algorithms is rarely compared against existing algorithms on real-world data. This study empirically evaluates 16 deep semi-supervised learning algorithms to fill the research gap. To investigate whether the algorithms perform differently in different scenarios, the algorithms are run on 15 commonly known datasets of three datatypes (image, text and sound). Since manual data labeling is expensive, practitioners must know how many manually labeled instances are needed to achieve the lowest error rates. Therefore, this study utilizes different configurations for the number of available labels to study the manual effort required for optimal error rate. Additionally, to study how different algorithms perform on real-world datasets, the researchers add noise to the datasets to mirror real-world datasets. The study utilizes the Bradley-Terry model to rank the algorithms based on error rates and the Binomial model to investigate the probability of achieving an error rate lower than 10%. The results demonstrate that utilizing unlabeled data with semi-supervised learning may improve classification accuracy over supervised learning. Based on the results, the authors recommend FreeMatch, SimMatch, and SoftMatch since they provide the lowest error rate and have a high probability of achieving an error rate below 10% on noisy datasets.

Context: Machine Learning Operations (MLOps) has become a top priority for companies. However, its adoption has become challenging due to the need for proper guidance and awareness. Most of the MLOps solutions available in the market are designed to fit the specific platform, tools and culture of the providers. Objective: The objective is to develop a structured approach to adopting, assessing and advancing MLOps adoption. Methods: The study was conducted based on a multi-case study across fourteen companies. Results: We provide a comprehensive analysis that highlights the similarities and differences in the adoption of MLOps practices among companies. We have also empirically validated the developed MLOps framework and MLOps maturity model. Furthermore, we carefully reviewed the feedback received from practitioners and revised the MLOps framework and maturity model to confirm its effectiveness. Additionally, we develop an MLOps taxonomy for classifying ML use cases based on their context and requirements into the desired stage of the MLOps framework and maturity model. Conclusion: The findings provide companies with a structured approach to adopt, assess, and further advance the adoption of MLOps practices regardless of their current status.

Context: The role of product management (PM) is key for building, implementing and managing softwareintensive systems. Whereas engineering is concerned with how to build systems, PM is concerned with 'what' to build and 'why' we should build the product. The role of PM is recognized as critical for the success of any product. However, few studies explore how the role of PM is changing due to recent trends that come with digitalization and digital transformation. Objectives: Although there is prominent research on PM, few studies explore how this role is changing due to the digital transformation of the software-intensive industry. In this paper, we study how trends such as DevOps and short feedback loops, data and artificial intelligence (AI), as well as the emergence of digital ecosystems, are changing current product management practices. Methods: This study employs a qualitative approach using multi-case study research as the method. For our research, we selected five case companies in the software-intensive systems domain. Through workshop sessions, frequent meetings and interviews, we explore how DevOps and short feedback loops, data and artificial intelligence (AI), and digital ecosystems challenge current PM practices. Results: Our study yielded an in-depth understanding of how digital transformation of the software-intensive systems industry is changing current PM practices. We present empirical results from workshops and from interviews in which case company representatives share their insights on how software, data and AI impact current PM practices. Based on these results, we present a framework organized along two dimensions, i.e. a certainty dimension and an approach dimension. The framework helps structure the approaches product managers can employ to select and prioritize development of new functionality. Contributions: The contribution of this paper is a framework for 'Strategic Digital Product Management' (SDPM). The framework outlines nine approaches that product managers can employ to maximize the return on investment (RoI) of R&D using new digital technologies.

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro-BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.

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.

Data is key for rapid and continuous delivery of customer value. By collecting data from products in the field, companies in the embedded systems domain can measure and monitor product performance and they get the opportunity to provide customers with insights and data-driven services. However, while the notion of data-driven development is not new, embedded systems companies are facing a situation in which data volumes are growing exponentially and this is not without its challenges. Suddenly, the cost of collecting, storing and processing data becomes a concern and while there is prominent research on different aspects of data-driven development, there is little guidance for how to reason about business value versus costs of data. In this paper, we present findings from case study research conducted in close collaboration with four companies in the embedded systems domain. The contribution of this paper is a framework that provides a holistic understanding of the multiple dimensions that need to be considered when reasoning about business value versus cost of collecting, storing and processing data.

DevOps has become a widely adopted approach in the software industry, especially among companies developing web-based applications. The main focus of DevOps is to address social and technical bottlenecks along the software flow, from the developers' code changes to delivering these changes to the production environments used by customers. However, DevOps does not consider the software flow's content, e.g., new features, bug fixes, or security patches, and the customer value of each content. In addition, DevOps assumes that a streamlined software flow leads to a continuous value flow, as customers use the new software and extract value-adding content intuitively. However, in a Software-intensive System of Systems (SiSoS), customers need to understand the content of the software flow to validate, test, and adopt their operation procedures before using the new software. Thus, while DevOps has been extensively studied in the context of web-based applications, its adoption in SiSoS is a relatively unexplored area. Therefore, we conducted a case study at a multinational telecommunications provider focusing on 5G systems. Our findings reveal that DevOps has three sub-flows: legacy, feature, and solution. Each sub-flow has distinct content and customer value, requiring a unique approach to extracting it. Our findings highlight the importance of understanding the software flow's content and how each content's value can be extracted when adopting DevOps in SiSoS.

For years, research on software ecosystems has focused on the many opportunities for stakeholders to engage in collaborative innovation and creation of new customer value. With a common platform as the basis, companies co-evolve capabilities around a shared set of technologies, knowledge, and skills, to develop new products and services that would have been difficult for the involved parties to realize internally. Previous studies present key elements for successful innovation ecosystems and strategies for when and how to align with external partners to accelerate innovation while maintaining competitive advantage. However, while research on innovation ecosystems is important for future value creation, research on ecosystems for managing commodity functionality is limited. Although commodity functionality constitutes a large part of existing systems, strategies for how to maintain, manage and evolve this type of functionality are often neglected and not paid as much attention. In this paper, and based on multi- case study research, we present a strategic framework that allows companies to systematically create a fully developed digital ecosystem around commodity functionality. The framework outlines a sequence of strategies starting from outsourcing and moving on to preferred supplier, supplier network and with the final stage being a fully developed ecosystem. 

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. As data security and privacy concerns continue to gain prominence, FL stands out as an option to enable organizations to leverage collective knowledge without compromising sensitive data. However, existing FL platforms and frameworks often present challenges for software engineers in terms of complexity, limited customization options, and scalability limitations. In this paper, we introduce EdgeFL, an edge-only lightweight decentralized FL framework, designed to overcome the limitations of centralized aggregation and scalability in FL deployments. By adopting an edge-only model training and aggregation approach, EdgeFL eliminates the need for a central server, enabling seamless scalability across diverse use cases. Our results show that EdgeFL reduces weights update latency and enables faster model evolution, enhancing the efficiency of edge model learning. Moreover, EdgeFL exhibits improved classification accuracy compared to traditional centralized FL approaches. By leveraging EdgeFL, software engineers can harness the benefits of Federated Learning while overcoming the challenges associated with existing FL platforms/frameworks.

Context: Federated Learning (FL) has gained prominence as a solution for preserving data privacy in machine learning applications. However, existing FL frameworks pose challenges for software engineers due to implementation complexity, limited customization options, and scalability issues. These limitations prevent the practical deployment of FL, especially in dynamic and resource-constrained edge environments, preventing its widespread adoption. Objective: To address these challenges, we propose EdgeFL, an efficient and low-effort FL framework designed to overcome centralized aggregation, implementation complexity and scalability limitations. EdgeFL applies a decentralized architecture that eliminates reliance on a central server by enabling direct model training and aggregation among edge nodes, which enhances fault tolerance and adaptability to diverse edge environments. Methods: We conducted experiments and a case study to demonstrate the effectiveness of EdgeFL. Our approach focuses on reducing weight update latency and facilitating faster model evolution on edge devices. Results: Our findings indicate that EdgeFL outperforms existing FL frameworks in terms of learning efficiency and performance. By enabling quicker model evolution on edge devices, EdgeFL enhances overall efficiency and responsiveness to changing data patterns. Conclusion: EdgeFL offers a solution for software engineers and companies seeking the benefits of FL, while effectively overcoming the challenges and privacy concerns associated with traditional FL frameworks. Its decentralized approach, simplified implementation, combined with enhanced customization and fault tolerance, make it suitable for diverse applications and industries.