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 fail...
Henning Femmer, Julian Frattini· IEEE International Requireme...· 0 citations
A harmonized perspective on digital nudging is introduced, in which multiple strategies are systematically integrated to provide consistent and context-aware decision support, in which multiple strategies are systematically integrated to provide consistent and context-aware decision support.
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...
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 applica...
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 practit...
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MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
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