Pekka Abrahamsson

Implementing Ethics in AI: Initial Results of an Industrial Multiple Case Study

Towards a Secure DevOps Approach for Cyber-Physical Systems: An Industrial Perspective

With the expansion of cyber-physical systems (CPSs) across critical and regulated industries, systems must be continuously updated to remain resilient. At the same time, they should be extremely secure and safe to operate and use. The DevOps approach caters to business demands of more speed and smartness in production, but it is extremely challenging to implement DevOps due to the complexity of critical CPSs and requirements from regulatory authorities. In this study, expert opinions from 33 European companies expose the gap in the current state of practice on DevOps-oriented continuous development and maintenance. The study contributes to research and practice by identifying a set of needs. Subsequently, the authors propose a novel approach called Secure DevOps and provide several avenues for further research and development in this area. The study shows that, because security is a cross-cutting property in complex CPSs, its proficient management requires system-wide competencies and capabilities across the CPSs development and operation.

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Startup Metrics That Tech Entrepreneurs Need to Know

Metrics can be used by firms to make more objective decisions based on data. Software startups in particular are characterized by the uncertain or even chaotic nature of the contexts in which they operate. Using data in the form of metrics can help software startups to make the right decisions amid uncertainty and limited resources. However, whereas conventional business metrics and software metrics have been studied in the past, metrics in the specific context of software startups have not been studied. In this chapter, we present the results of a multivocal literature review to offer you 118 metrics practitioner experts think software startups should measure. These metrics can give you ideas for what your startup should measure.

A Multiple Case Study of Artificial Intelligent System Development in Industry

There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.

Software Startup Practices - Software Development in Startups Through the Lens of the Essence Theory of Software Engineering

. Software startups continue to be important drivers of economy globally. As the initial investment required to found a new software company becomes smaller and smaller resulting from technological advances such as cloud technology, increasing numbers of new software startups are born. Startups are considered to differ from other types of software organizations in various ways, including software development. In this paper, we study software development in startups from the point of view of practices to better understand how startups develop software. Using extant literature and case study data, we devise a list of practices which we categorize using the Essence Theory of Software Engineering (Essence). Based on the data, we propose a list of common practices utilized by software startups. Additionally, we propose potential changes to Essence to make it better suited for the software startup context.

The entrepreneurial logic of startup software development: A study of 40 software startups

Software startups are an essential source of innovation and software-intensive products. The need to understand product development in startups and to provide relevant support are highlighted in software research. While state-of-the-art literature reveals how startups develop their software, the reasons why they adopt these activities are underexplored. This study investigates the tactics behind software engineering (SE) activities by analyzing key engineering events during startup journeys. We explore how entrepreneurial mindsets may be associated with SE knowledge areas and with each startup case. Our theoretical foundation is based on causation and effectuation models. We conducted semi-structured interviews with 40 software startups. We used two-round open coding and thematic analysis to describe and identify entrepreneurial software development patterns. Additionally, we calculated an effectuation index for each startup case. We identified 621 events merged into 32 codes of entrepreneurial logic in SE from the sample. We found a systemic occurrence of the logic in all areas of SE activities. Minimum Viable Product (MVP), Technical Debt (TD), and Customer Involvement (CI) tend to be associated with effectual logic, while testing activities at different levels are associated with causal logic. The effectuation index revealed that startups are either effectuation-driven or mixed-logics-driven. Software startups fall into two types that differentiate between how traditional SE approaches may apply to them. Effectuation seems the most relevant and essential model for explaining and developing suitable SE practices for software startups.

Software - A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]

How modeling helps in developing self-sovereign identity governance framework: An experience report

Digital Identity has become a topic that attracts the attention of researchers due to the enormous number of services that have been provided online recently. Researchers face many obstacles regarding the security, privacy

StartCards - A method for early-stage software startups

course of 4 AR cycles. During the AR process, the method was used by 44 student startup teams in a practical course setting. Data from the use of the method was collected through self-reporting in the form of modified learning diaries, mentoring meetings with the startup teams, and a qualitative survey. Results: We consider the current version of StartCards useful for early-stage startups based on the data we have collected. The method can also be used as a pedagogical tool in startup education. Conclusions: The paper presents the first published version of the method. While work on the method continues, the method is deemed ready for use.

Preface of RESET 2023: 2nd International Workshop on Requirement Engineering for Software Startups and Emerging Technologies

The Second International Workshop on Requirement Engineering for Software startups and Emerging Technologies (RESET) is a part of the 31st IEEE International Requirements Engineering Conference 2023, held on 4 September 2023. The workshop brought together requirements engineering researchers and practitioners to discuss the need for adapting conventional requirement engineering artifacts (i.e., requirement definition, metrics), processes and practices in developing and operating emerging technologies, including Software Startups, Artificial Intelligence (AI), Blockchain, and Quantum Computing. Participants gained insights into the RE practices, tools, techniques, and frameworks that can help them build scalable, robust, and innovative software-intensive systems. The workshop included a keynote presentation and four paper presentations.

Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies

The restrictions imposed by the COVID-19 pandemic required software development teams to adapt, being forced to work remotely and adjust the software engineering activities accordingly. In the studies evaluating these effects, a few have assessed the impact on software engineering activities from a broader perspective and after a period of time when teams had time to adjust to the changes. No studies have been found comparing software startups and established companies either. This paper aims to investigate the impacts of COVID-19 on software development activities after one year of the pandemic restrictions, comparing the results between startups and established companies. Our approach was to design a cross-sectional survey and distribute it online among software development companies worldwide. The participants were asked about their perception of COVID-19’s pandemic impact on different software engineering activities: requirements engineering, software architecture, user experience design, software implementation, and software quality assurance. The survey received 170 valid answers from 29 countries, and for all the software engineering activities, we found that most respondents did not observe a significant impact. The results also showed that software startups and established companies were affected differently since, in some activities, we found a negative impact in the former and a positive impact in the latter. Regarding the time spent on each software engineering activity, most of the answers reported no change, but on those that did, the result points to an increase in time. Thus, we cannot find any relation between the change in time of effort and the reported positive or negative impact.