Automation and Artificial Intelligence in Software Engineering: Experiences, Challenges, and Opportunities

Automation and Artificial Intelligence have a transformative influence on many sectors, and software engineers are the actors who engineer this transformation. On the other hand, there is little knowledge of how automation and Artificial Intelligence impact software engineering practice. To answer this question, we conducted semi-structured interviews with experienced software practitioners across frontend and backend development, DevOps, R&D, integration, and leadership positions. Our findings reveal 1) automation to appear as micro-automation in the sense of automation of tiny and specific tasks, 2) automation as a side product of work, and bottom-up driven in software engineering, and 3) automation as a possible cause for cognitive overhead due to automatically generated notifications. Furthermore, we notice that our interview participants do not expect automation and artificial intelligence tools to substantially change software engineering\u27s essence in the foreseeable future

Special Issue on: Awareness and Reflection in Technology Enhanced Learning. Vol.9, (2-3)

Awareness and reflection play a crucial role in the learning process, helping the involved actors to succeed in self-regulated learning and to optimise their learning experience. Whether in traditional education, workplace training or lifelong learning, appropriate feedback together with proper assessment of previous practices can bring benefits for all the participants and cultivate their reflective skills, which are essential for effective learning

The Data Product Canvas - A Visual Collaborative Tool for Designing Data-Driven Business Models

The availability of data sources and advances in analytics and artificial intelligence offers the opportunity for organizations to develop new data-driven products, services and business models. Though, this process is challenging for traditional organizations, as it requires knowledge and collaboration from several disciplines such as data science, domain experts, or business perspective. Furthermore, it is challenging to craft a meaningful value proposition based on data; whereas existing research can provide little guidance. To overcome those challenges, we conducted a Design Science Research project to derive requirements from literature and a case study, develop a collaborative visual tool and evaluate it through several workshops with traditional organizations. This paper presents the Data Product Canvas, a tool connecting data sources with the user challenges and wishes through several intermediate steps. Thus, this paper contributes to the scientific body of knowledge on developing data-driven business models, products and services

Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment

Haptic displays are commonly limited to transmitting a discrete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the linear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and personalisation through sensitivity adjustment increased decoding precision

Supporting Data-Driven Business Model Innovations: A structured literature review on tools and methods

Purpose: This paper synthesizes existing research on tools and methods that support data-driven business model innovation, and maps out relevant directions for future research. Design/methodology/approach: We have carried out a structured literature review and collected and analysed a respectable but not excessively large number of 33 publications, due to the comparatively emergent nature of the field. Findings: Current literature on supporting data-driven business model innovation differs in the types of contribution (taxonomies, patterns, visual tools, methods, IT tool and processes), the types of thinking supported (divergent and convergent) and the elements of the business models that are addressed by the research (value creation, value capturing and value proposition). Research limitations/implications: Our review highlights the following as relevant directions for future research. Firstly, most research focusses on supporting divergent thinking, i.e. ideation. However, convergent thinking, i.e. evaluating, prioritizing, and deciding, is also necessary. Secondly, the complete procedure of developing data-driven business models and also the development on chains of tools related to this have been under-investigated. Thirdly, scarcely any IT tools specifically support the development of data-driven business models. These avenues also highlight the necessity to integrate between research on specifics of data in business model innovation, on innovation management, information systems and business analytics. Originality/value: This paper is the first to synthesize the literature on how to identify and develop data-driven business models, and to map out (interdisciplinary) research directions for the community. Keywords: Business model innovation, data-driven business models, research agenda.   Article classification: Literature revie

How large manufacturing firms understand the impact of digitization: ALearning Perspective

Digitization is currently one of the major factors changing society and the business world. Most research focused on the technical issues of this change, but also employees and especially the way how they learn changes dramatically. In this paper, we are interested in exploring the perspectives of decision makers in huge manufacturing companies on current challenges in organizing learning and knowledge distribution in digitized manufacturing environments. Moreover, we investigated the change process and challenges of implementing new knowledge and learning processes. To this purpose, we have conducted 24 interviews with senior representatives of large manufacturing companies from Austria, Germany, Italy, Liechtenstein and Switzerland. Our exploratory study shows that decision makers perceive significant changes in work practice of manufacturing due to digitization and they currently plan changes in organizational training and knowledge distribution processes in response. Due to the lack of best practices, companies focus very much on technological advancements. The delivery of knowledge just-in-time directly into work practice is a favorite approach. Overall, digital learning services are growing and new requirements regarding compliance, quality management and organisational culture arise

Designing Technologies to Support Professional and Workplace Learning for Situated Practice

The papers in this special section focus on designing technologies to support professional and workplace learning in situated practices. In an era of global, organizational, and technological change, all of which are transforming the world of work, professional and workplace learning are critical for employability and organizational competitiveness. A range of fundamental transformations is changing how people work. Digital technologies are replacing human labor and, at the same time, are accelerating the expansion of job roles and work practices. Work is becoming increasingly specialized, which means that professionals in collaborative and networked ways across discipline and organization boundaries. In parallel, labor is increasingly decentralized, making decisionmaking more distributed and raising the need for remote communication and collaboration. Subsequently, work is becoming more independent from time and place, as people connect, collaborate, and work via digital technologies. These changes come with a need for substantial and continuous workplace learning, and with the need for changes in how workplace learning happens. Of course, digital technologies are already used to provide learning and training in workplaces. However, most of these learning technologies have been developed for formal education (e.g., K- 12 and higher education) rather than in workplace contexts. There is a need to understand and evidence workplace learning needs and to further develop technologies that can support and scale workplace learning