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machine learning

6,549 papers

#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Open access 2014

Towards Abstraction and Automation in Software Engineering

The results of this study show that the Ball ecosystem has the potential to improve the productivity of software development, however, it should produce smaller and more reasonable software systems, leading to a better reusability and a shorter learning phase for new developers.

Michael Gurschler, Henry Edison, Kalle Launiala et al. · 1 citation
#machine learning Open access Sep 2015

A Simultaneous, Multidisciplinary Development and Design Journey - Reflections on Prototyping

A wayfaring approach for the early concept creation stage of development projects that have a very high degree of intended innovation and thus uncertainty and the importance of including all the involved disciplines (knowledge domains) from the beginning of the project on.

Achim Gerstenberg, Heikki Sjöman, Thov Reime et al. · 36 citations · ⚡4

What leads developers towards the choice of a JavaScript framework?

A model of factors that are desirable to be found in a JSF and a representation of the decision makers involved in the frameworks selection is offered, which contributes to the body of knowledge related to the decision-making process when selecting aJSF.

Amantia Pano, D. Graziotin, P. Abrahamsson · 7 citations · ⚡1

Time for AI (Ethics) Maturity Model Is Now

It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.

Ville Vakkuri, Marianna Jantunen, Erika Halme et al. · 17 citations · ⚡1
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

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.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#machine learning Book Open access May 2019

An Empirical Study on Female Participation in Software Project Courses

Gender issues in software engineering education are gaining research attention due to the desire to promote female participation in the field. The objective of this work is to enhance the understanding of female students' participation in software engineering projects to support gender-aware course optimization. Since 2015, we have investigated the participation of female students in terms of software engineering activities and team dynamics in a software project course that involves a real customer. We found that female students are more active with project management and requirement engineering, while they remain under-represented in highly complex or specific tasks, i.e. architecture work, and user experience design. We found no statistically significant difference in perceived team dynamics between male and female students. Insights on female project activities would facilitate the arrangement of project teams so that learning can be distributed equally across genders

Anh Nguyen-Duc, M. L. Jaccheri, P. Abrahamsson · 9 citations

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