Managing Organizatinal Resources as Platform Boundary Resources

Approaching digital innovation via digital platforms shifts firms’ locus of attention to the different actors in their ecosystems. Firms tend to empower the platform’s ecosystem through expanding developer contribution via introducing boundary resources such as application programming interfaces (APIs). This study addresses the challenge of platform owners managing their internal assets as platform boundary resources. We seek to answer how platform owners can identify and visualize values of potential boundary resources by conducting a single case study at a large international company active in embeded software development area. This study suggests e3 value modelling as a tool to assist platform owners in understanding the platform ecosystem actors, the values of assets for the ecosystem and how these values can be interchanged among the actors

Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes

Natural Language Processing (NLP) for Requirements Engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models (LLM) like ChatGPT have gained significant recognition due to their notably improved performance in NLP tasks. To explore the potential of ChatGPT to assist in requirements elicitation processes, we formulated six questions to elicit requirements using ChatGPT. Using the same six questions, we conducted interview-based surveys with five RE experts from academia and industry and collected 30 responses containing requirements. The quality of these 36 responses (human-formulated + ChatGPT-generated) was evaluated over seven different requirements quality attributes by another five RE experts through a second round of interview-based surveys. In comparing the quality of requirements generated by ChatGPT with those formulated by human experts, we found that ChatGPT-generated requirements are highly Abstract, Atomic, Consistent, Correct, and Understandable. Based on these results, we present the most pressing issues related to LLMs and what future research should focus on to leverage the emergent behaviour of LLMs more effectively in natural language-based RE activities.Comment: Accepted at SEAA 2023. 8 pages, 5 figure

Interview with Anne Persson on “The Practice of Enterprise Modeling”

We conducted the interview iteratively via email correspondence over the summer of 2017. Anne had been the general chair of PoEM 2017 in Skövde 2016 and, given her history with PoEM, we thus were very keen to learn about her views on enterprise modeling

iStar 2.0 language guide

The i* modeling language was introduced to fill the gap in the spectrum of conceptual modeling languages, focusing on the intentional (why?), social (who?), and strategic (how? how else?) dimensions. i* has been applied in many areas, e.g., healthcare, security analysis, eCommerce. Although i* has seen much academic application, the diversity of extensions and variations can make it difficult for novices to learn and use it in a consistent way. This document introduces the iStar 2.0 core language, evolving the basic concepts of i* into a consistent and clear set of core concepts, upon which to build future work and to base goal-oriented teaching materials. This document was built from a set of discussions and input from various members of the i* community. It is our intention to revisit, update and expand the document after collecting examples and concrete experiences with iStar 2.0.Preprin

Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest

Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain

T-Reqs: Tool Support for Managing Requirements in Large-Scale Agile System Development

T-Reqs is a text-based requirements management solution based on the git version control system. It combines useful conventions, templates and helper scripts with powerful existing solutions from the git ecosystem and provides a working solution to address some known requirements engineering challenges in large-scale agile system development. Specifically, it allows agile cross-functional teams to be aware of requirements at system level and enables them to efficiently propose updates to those requirements. Based on our experience with T-Reqs, we i) relate known requirements challenges of large-scale agile system development to tool support; ii) list key requirements for tooling in such a context; and iii) propose concrete solutions for challenges.Comment: Accepted for publication in Proc. of 26th IEEE Int. Requirements Eng. Conf., Demo Track, Banff, Alberta, Canada, 201