May 2017· International Conference on Agile Software Development· pp. 20-36· 44 citations· ⚡ 5 influential· 33 references
Computer Science
TL;DR
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Abstract
It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds; firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startup’s progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping.
Interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023 offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.
Benyamin T. Tabarsi, Heidi Reichert, Sam Gilson et al.· Empirical Software Engineeri...· 22 citations· ⚡1
A novel holistic theory of requirements engineering (RE) quality is proposed that can serve as a coherent theoretical framework for understanding the success or failure of RE processes and artifacts, and it is envisioned that the theory can serve as a coherent theoretical framework for understanding the success or failure of RE processes and artifacts.
Henning Femmer, Julian Frattini· IEEE International Requireme...· 0 citations
Digital nudging, a behavioral approach that subtly guides decision-making, is gaining increasing attention in software development as a means to support developers in complex and cognitively demanding tasks. This article analyzes the evolution of digital nudging and persuasive technology in software engineering from 2010 to April 2026. Based on a structured literature review, a temporal heatmap and qualitative trend analysis are used to examine how established nudging strategies, such as feedback, framing, and personalization, have been applied to support software development activities. The results demonstrate a shift from rule-based and socially oriented interventions toward more adaptive, context-aware, and AI/LLM-mediated approaches. In particular, recent work shows an increasing integration of personalized and real-time nudging mechanisms directly into development environments. Building on these findings, the article introduces a harmonized perspective on digital nudging, in which multiple strategies are systematically integrated to provide consistent and context-aware decision support. An illustrative AI-driven plug-in prototype demonstrates how personalized nudging mechanisms can be integrated into a development environment to support software quality and adherence to design principles such as SOLID. Furthermore, key challenges are identified, including the selection and combination of nudging strategies, their evaluation, and their integration into real-world development workflows. Overall, the article contributes a structured synthesis of existing research and outlines directions for future work on harmonized, AI-driven nudging in software engineering.
I. Pribik, Alexander Felfernig· Frontiers in Artificial Inte...· 0 citations
Part II of a two-course entrepreneurial capstone framework, addressing the transition from opportunity identification to engineered prototype realization. Building on outputs from Part I (user stories supporting evidence, and a conceptual prototype), this course teaches a lightweight, systems engineering framework that preserves iterative momentum while enforcing traceability from user requirements to engineering specifications to evidence-based verification and validation. A simplified Quality Function Deployment matrix links user stories to four classes of engineering specifications, guiding the architecture, detailed design, and test plan. The course distinguishes technical verification from user validation to operationalize an evidence-based definition of “done” and integrates sprint-based prototyping with review gates. Targeted entrepreneurship instruction (branding, financial analysis, pricing, and pitching) refines venture documentation once a product is under development. This paper presents the design criteria, framework, and assessment structure supporting these outcomes.
K. Bubbar, Benjamin DeBoer· Proceedings of the Canadian...· 0 citations
Prototyping has evolved from a simple representational artifact into a central mechanism for learning, communication, risk reduction, and decision-making in engineering design. Despite its widespread adoption across engineering disciplines, existing research remains fragmented across domains, methodologies, and application contexts, making it difficult to distinguish broadly applicable principles from context-specific practices. This paper presents a critical review of prototyping research in engineering design, synthesizing evidence from peer-reviewed journal articles and conference papers from foundational studies of the 1980s to recent developments in rapid prototyping, additive manufacturing, digital engineering, and Industry 4.0 systems. A thematic literature review was conducted to identify recurring principles, domain-dependent variations, emerging trends, and persistent limitations in current prototyping practices. The review examines key factors influencing prototyping effectiveness, including purpose, fidelity, timing, stakeholder involvement, modeling and analysis, risk management, economic considerations, and learning-oriented iteration. Particular attention is given to how uncertainty influences prototyping decisions and the ways in which different uncertainty conditions influence the selection, scope, and implementation of prototyping activities. The findings indicate that prototyping is best understood as a context-dependent decision-support activity whose effectiveness depends on the uncertainties, constraints, stakeholders, and design objectives associated with a specific engineering problem. Although several common principles emerge across engineering domains, substantial differences exist in how prototypes are used to support design decisions and system validation. The review identifies research gaps related to uncertainty-driven fidelity selection, integration of modeling, experimentation, and verification activities, and the limited availability of systematic guidance for selecting prototyping strategies across diverse engineering contexts. Future research should focus on generalized prototyping frameworks, quantitative decision-support methods for uncertainty management, enhanced stakeholder integration, and the continued convergence of physical and virtual prototyping environments in next-generation engineering systems.
R. Landaeta, A. Zabihollah, R. Jazar· Applied Sciences· 0 citations
While it is fairly well known how empirical software engineering (ESE) is used in the academic world, we have limited knowledge of how ESE is practiced in industry. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we want to dedicate a series of articles to interviewing ESE practitioners from various companies. Among other things, we want to understand how ESE processes are implemented in industry, e.g., different research methods, how practitioners decide on what to study, how research results are used within companies and beyond, and if they face recurrent impediments to using ESE methods in industrial contexts. In the first edition of"ESE in Practice", we are joined by Ciera Jaspan and Collin Green from the Developer Intelligence team at Google. This article is a faithful account of our conversation from August 13, 2026, which we edited for our column.
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