Kari Smolander

Muhammad Azeem Akbar, Matteo Esposito, Sami Hyrynsalmi et al.

In the era of 6G, developing and managing software requires cutting-edge software engineering (SE) theories and practices tailored for such complexity across a vast number of connected edge devices. Our project aims to lead the development of sustainable methods and energy-efficient orchestration models specifically for edge environments, enhancing architectural support driven by AI for contemporary edge-to-cloud continuum computing. This initiative seeks to position Finland at the forefront of the 6G landscape, focusing on sophisticated edge orchestration and robust software architectures to optimize the performance and scalability of edge networks. Collaborating with leading Finnish universities and companies, the project emphasizes deep industry-academia collaboration and international expertise to address critical challenges in edge orchestration and software architecture, aiming to drive significant advancements in software productivity and market impact.

Katja Karhu, Kari Smolander, Jussi Kasurinen

There has been intense debate among qualitative researchers about whether generative AI is suitable for qualitative research. In this paper, we summarize the broader ongoing discussion of generative AI in qualitative research and its implications for software engineering researchers. The qualitative research approach, small-q (positivist or post-positivist) or Big Q (non-positivist), is among the major criteria for determining whether generative AI can be used in qualitative research. In addition to research philosophy and research approach, skills, ethics, and personal preferences also play a role in researchers' decisions about whether to use AI in qualitative research.

Katja Karhu, Jussi Kasurinen, Kari Smolander

In the software industry, artificial intelligence (AI) has been utilized more and more in software development activities. In some activities, such as coding, AI has already been an everyday tool, but in software testing activities AI it has not yet made a significant breakthrough. In this paper, the objective was to identify what kind of empirical research with industry context has been conducted on AI in software testing, as well as how AI has been adopted in software testing practice. To achieve this, we performed a systematic mapping study of recent (2020 and later) studies on AI adoption in software testing in the industry, and applied thematic analysis to identify common themes and categories, such as the real-world use cases and benefits, in the found papers. The observations suggest that AI is not yet heavily utilized in software testing, and still relatively few studies on AI adoption in software testing have been conducted in the industry context to solve real-world problems. Earlier studies indicated there was a noticeable gap between the actual use cases and actual benefits versus the expectations, which we analyzed further. While there were numerous potential use cases for AI in software testing, such as test case generation, code analysis, and intelligent test automation, the reported actual implementations and observed benefits were limited. In addition, the systematic mapping study revealed a potential problem with false positive search results in online databases when using the search string "artificial intelligence".

AKM Bahalul Haque, AKM Najmul Islam, Sami Hyrynsalmi et al.

Although blockchain-based digital services promise trust, accountability, and transparency, multiple paradoxes between blockchains and GDPR have been highlighted in the recent literature. Some of the recent literature also proposed possible solutions to these paradoxes. This article aims to conduct a systematic literature review on GDPR compliant blockchains and synthesize the findings. In particular, the goal was to identify 1) the GDPR articles that have been explored in prior literature; 2) the relevant research domains that have been explored, and 3) the research gaps. Our findings synthesized that the blockchains relevant GDPR articles can be categorized into six major groups, namely data deletion and modification (Article 16, 17, and 18), protection by design by default (Article 25), responsibilities of controllers and processors (Article 24, 26, and 28), consent management (Article 7), data processing principles and lawfulness (Article 5,6 and 12), and territorial scope (Article 3). We also found seven research domains where GDPR compliant blockchains have been discussed, which include IoT, financial data, healthcare, personal identity, online data, information governance, and smart city. From our analysis, we have identified a few key research gaps and present a future research direction.

Mehar Ullah, Pedro H. J. Nardelli, Annika Wolff et al.

Internet of Things (IoT) refers to the interconnection of physical objects via the Internet. It utilises complex back-end systems which need different capabilities depending on the requirements of the system. IoT has already been used in various applications, such as agriculture, smart home, health, automobiles, and smart grids. There are many IoT platforms, each of them capable of providing specific services for such applications. Finding the best match between application and platform is, however, a hard task as it can difficult to understand the implications of small differences between platforms. This paper builds on previous work that has identified twenty-one important factors of an IoT platform, which were verified by Delphi method. We demonstrate here how these factors can be used to discriminate between five well known IoT platforms, which are arbitrarily chosen based on their market share. These results illustrate how the proposed approach provides an objective methodology that can be used to select the most suitable IoT platform for different business applications based on their particular requirements.

Ville Alkkiomäki, Kari Smolander

Service-oriented computing has created new requirements for information systems development processes and methods. The adoption of service-oriented development requires service identification methods matching the challenge in enterprises. A wide variety of service identification methods (SIM) have been proposed, but less attention has been paid to the actual requirements of the methods. This paper provides an ethnographical look at challenges in service identification based on data from 14 service identification sessions, providing insight into the practice of service identification. The findings identified two types of service identification sessions and the results can be used for selecting the appropriate SIM based on the type of the session.