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As Large Language Models (LLMs), including ChatGPT and analogous systems, continue to advance, their robust natural language processing capabilities and diverse applications have garnered considerable attention. Nonetheless, despite the increasing acknowledgment of the convergence of Artificial Intelligence (AI) and Software Engineering (SE), there is a lack of studies involving the impact of this convergence on the practices and perceptions of software developers. Understanding how software developers perceive and engage with AI tools, such as ChatGPT, is essential for elucidating the impact and potential challenges of incorporating AI-driven tools in the software development process. In this paper, we conducted a survey with 207 software developers to understand the impact of ChatGPT on software quality, productivity, and job satisfaction. Furthermore, the study delves into developers' expectations regarding future adaptations of ChatGPT, concerns about potential job displacement, and perspectives on regulatory interventions., Comment: 31 pages, 9 figures

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.

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning, addressing data privacy concerns. 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. With a straightforward integration process requiring just four lines of code (LOC), software engineers can easily incorporate FL functionalities into their AI products. Furthermore, EdgeFL offers the flexibility to customize aggregation functions, empowering engineers to adapt them to specific needs. Based on the results, we demonstrate that EdgeFL achieves superior performance compared to existing FL platforms/frameworks. Our results show that EdgeFL reduces weights update latency and enables faster model evolution, enhancing the efficiency of edge devices. 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.

For companies developing web-based applications, the Dev and the Ops refer to different groups with either operational or development focus. Therefore, DevOps help these companies streamline software development and operations activities by emphasizing the collaboration between the two groups. However, for companies producing software-intensive products, the Ops would refer to customers who use and operate the product. In addition, companies producing software-intensive products do not only offer products to customers but rather Product Service Systems (PSS), where product-related services play a key role in ensuring customer satisfaction besides their significant revenue contribution. Thus, the context of product-oriented PSS is very different from web-based applications, making it difficult to apply DevOps without considering the role of the 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. Services become the glue that connects the Dev and the Ops, achieved by providing internal services to increase the precision of the development organization and external services to increase the speed of deployment and new content adoption on the customers' side.

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating software changes. In the automotive domain, running randomised field experiments is not always desired, possible, or even ethical. In the face of such limitations, we develop a framework BOAT (Bayesian causal modelling for ObvservAtional Testing), utilising observational studies in combination with Bayesian causal inference, in order to understand real-world impacts from complex automotive software updates and help software development organisations arrive at causal conclusions. In this study, we present three causal inference models in the Bayesian framework and their corresponding cases to address three commonly experienced challenges of software evaluation in the automotive domain. We develop the BOAT framework with our industry collaborator, and demonstrate the potential of causal inference by conducting empirical studies on a large fleet of vehicles. Moreover, we relate the causal assumption theories to their implications in practise, aiming to provide a comprehensive guide on how to apply the causal models in automotive software engineering. We apply Bayesian propensity score matching for producing balanced control and treatment groups when we do not have access to the entire user base, Bayesian regression discontinuity design for identifying covariate dependent treatment assignments and the local treatment effect, and Bayesian difference-in-differences for causal inference of treatment effect overtime and implicitly control unobserved confounding factors. Each one of the demonstrative case has its grounds in practise, and is a scenario experienced when randomisation is not feasible. With the BOAT framework, we enable online software evaluation in the automotive domain without the need of a fully randomised experiment., Comment: In submission

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.

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible