Kari Smolander

Context: Serverless computing, also referred to as Function‐as‐a‐Service (FaaS), has revolutionized application development by abstracting the management of underlying infrastructure. Accordingly, the software industry frequently adopts open‐source serverless frameworks such as OpenFaaS and Apache OpenWhisk for greater portability and flexibility. However, this adaptability often introduces a range of development and operational issues during application development. Objective: To this end, this study aims to develop an empirically grounded understanding of the issues encountered when developing applications with open‐source serverless frameworks, their underlying causes, and the relationships between them. Method: We conducted an empirical investigation of 3550 closed issue reports from 12 open‐source serverless frameworks. Results: Our analysis led to the development of a comprehensive taxonomy of 261 issue types and 158 underlying cause types that developers encounter when developing applications with these frameworks. The findings revealed that Development Workflow, Release Workflow and Containerization, and System Configuration and Interoperability are the leading categories of issues, whereas Improper Development and Execution Practices, Missing Components and Critical Features, and Mismanagement of Versioning and Variables are the most prevalent underlying causes of these issues. We further develop an issue–cause mapping that links observed issues to their underlying causes and identify which issues are most strongly shaped by the serverless execution model. Conclusion: Finally, we discuss implications for researchers and practitioners, propose actionable tooling directions derived from the issue–cause mapping, and outline future research directions based on the findings of this study.

This paper analyzes stakeholders’ understanding of an electronic identification (e-ID) system based on artificial intelligence and distributed ledger technology. We address the question "How is the trustworthiness of a novel information system for e-ID influenced by the stakeholders’ understanding of its base technologies?". Our findings are based on a qualitative analysis of a questionnaire and interviews with stakeholders from a system development project focused on e-ID for online public services. We found that current e-ID systems have good usability but lack dialog and feedback mechanisms, whereas technical robustness and data protection are deemed essential attributes of emerging solutions. We identified four profiles of prospective users according to variations of trust in the new e-ID system. These findings suggest the need for greater transparency to facilitate the adoption of nascent digital identity solutions.

Background: Generative artificial intelligence (GAI) is expected to enhance the productivity of the public social and health care sector while maintaining, at minimum, current standards of quality and user experience. However, empirical evidence on GAI impacts in practical, real-life settings remains limited. Objective: This study investigates productivity, machine translation quality, and user experience impacts of the GPT-4 language model in an in-house language translation services team of a large well-being services county in Finland. Methods: A mixed methods study was conducted with 4 in-house translators between March and June 2024. Quantitative data of 908 translation segments were collected in real-life conditions using the computer-assisted language translation software Trados (RWS) to assess productivity differences between machine and human translation. Quality was measured using 4 automatic metrics (human-targeted translation edit rate, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Character n-gram F-score) applied to 1373 GAI-human segment pairs. User experience was investigated through 5 semistructured interviews, including the team supervisor. Results: The findings indicate that, on average, postediting machine translation is 14% faster than translating texts from scratch (2.75 vs 2.40 characters per second, P=.03), and up to 37% faster when the number of segments is equalized across translators. However, productivity varied notably between individuals, with improvements ranging from −2% to 102%. Regarding translation quality, 11% (141/1261) of Finnish-Swedish and 16% (112) of Finnish-English GAI outputs were accepted without edits. Average human-targeted translation edit rate scores were 55 (Swedish) and 46 (English), indicating that approximately half of the words required editing. Bilingual Evaluation Understudy scores averaged 43 for Swedish and 38 for English, suggesting good translation quality. Metric for Evaluation of Translation With Explicit Ordering and Character n-gram F-scores reached 63 and 68 for Swedish and 59 and 57 for English, respectively. All metrics have been converted to an equivalent scale from 0 to 100, with 100 reflecting a perfect match. Interviewed translators expressed mixed reviews on productivity gains but generally perceived value in using GAI, especially for repetitive, generic content. Identified challenges included inconsistent or incorrect terminology, lack of document-level context, and limited system customization. Conclusions: Based on this case study, GPT-4–based GAI shows measurable potential to enhance translation productivity and quality within an in-house translation team in the public social and health care sector. However, its effectiveness appears to be influenced by factors, such as translator postediting skills, workflow design, and organizational readiness. These findings suggest that, in similar contexts, public social and health care organizations could benefit from investing in translator training, optimizing technical integration, redesigning workflows, and implementing effective change management. Future research should examine larger translator teams to assess the generalizability of these results and further explore how translation quality and user experience can be improved through domain-specific customization.

Serverless computing has revolutionized cloud computing by abstracting the management of underlying infrastructure. This paradigm shift allows developers to concentrate on developing and optimizing application logic rather than managing the complexities of servers. Platforms such as AWS Lambda and OpenWhisk are gaining widespread adoption by enabling organizations to execute code in response to events. However, selecting the right serverless platform becomes challenging for organizations with the increasing number of serverless platforms and their diverse features. Moreover, decision-makers often lack specialized expertise in each platform, compelling them to continuously adapt to the rapidly evolving information and frequent updates. Therefore, this study presents a decision model for the serverless platforms selection problem that systematically captures knowledge and key features required for selecting serverless platforms. The model was evaluated through five real-world case studies at different software development companies. It suggests and prioritizes more than one serverless platform based on the participants' requirements. Participants reported that the model offered valuable insights, significantly simplifying the selection process while reducing the time and costs associated with decision-making. Additionally, the model enables organizations to efficiently evaluate and select serverless platforms that align with their technical requirements and strategic objectives. It also provides a foundation of reusable insights for researchers addressing future challenges in serverless computing.

Organizations adopt technological innovations to improve efficiency, gain productivity, and generate competitive advantage. Innovative technologies have evolved information technology (IT) infrastructure in recent decades. This paper determines the influence of motivational drivers in technology adoption decisions of organizations. The grounded theory method is used by collecting data through semi-structured questionnaires from 21 practitioners who participated in the interviews from 13 Finnish organizations. In this paper, we present organization-based findings that identify 6 origins of motivational drivers (triggers) by discussing associations between them and their positive & negative effects (consequences) on technology adoption. Our findings help practitioners in decision-making regarding technology adoption by highlighting the rationales and their causes within the organizational context.