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climate science

381 papers

#computer vision Jun 2013

Exploring How Feature Usage Relates to Customer Perceived Value: A Case Study in a Startup Company

This paper investigates how usage of features relate to their perceived value and shed light into the factors affecting this relationship and shows that feature usage metric has a significant potential to estimate value of features.

Sarunas Marciuska, Çigdem Gencel, P. Abrahamsson · 15 citations
#computer vision Jun 2013

Towards a Conceptual Framework for Assessing the Benefits of Cloud Computing

A conceptual framework of cloud computing benefits is proposed, based on various benefit taxonomies presented in the Information System literature, and accounts for the different business areas and organizational levels where each of the benefits manifests.

Nattakarn Phaphoom, Xiaofeng Wang, P. Abrahamsson · 7 citations
#computer vision Jun 2013

Feature Usage Diagram for Feature Reduction

A modelling notation, so called Feature Usage Diagram, and an approach to identify and visualize the required information for decision makers when reducing features, which has potential to support developers when making decisions for feature reduction.

Sarunas Marciuska, Çigdem Gencel, Xiaofeng Wang et al. · 5 citations
#computer vision Dec 2013

Agile Project - An Oxymoron? Proposing an Unproject Leadership Model for Complex Space

This paper proposes a novel approach called the unproject leadership model, which translates and maps the specific complexity concepts to the software development domain, consolidating them with the contributions already achieved by the lean and agile literature.

Juha Rikkilä, Xiaofeng Wang, P. Abrahamsson · 0 citations
#computer vision Sep 2013

Measuring the Success of Software Process Improvement: The Dimensions

Early results indicate that change agents valued the process user satisfaction the most and the process improvement fit the least, confirming the need of having various stakeholders and dimensions acknowledged in a framework that is used to measure the overall success of an SPI initiative.

P. Abrahamsson · 12 citations · ⚡1
#computer vision Conference Oct 2014

Software Product Size Measurement Methods: A Systematic Mapping Study

This study presents the results of a systematic mapping study on software size estimation metrics and methods. The methods are investigated based on the entity type(s) measured by the method, size attribute type, size measure(s), and theoretical/empirical validation status of the method, and restriction with respect to...

Sohaib Shahid Bajwa, Çigdem Gencel, P. Abrahamsson · 10 citations

Self-organized Learning in Software Factory: Experiences and Lessons Learned

A set of themes that can potentially explain self-organization from the learning viewpoint are identified, which include self-decided learning goals and personalized learning outcomes, peer teaching through active collaboration, diversity is the key and the personal attitude towards the learning matters.

Xiaofeng Wang, M. I. Lunesu, Juha Rikkilä et al. · 6 citations
#computer vision Aug 2015

Performance Alignment Work: How software developers experience the continuous adaptation of team performance in Lean and Agile environments

This paper aims to understand how software developers experience the continuous adaptation of performance in a modern, highly volatile environment using Lean and Agile software development methodology and generates a grounded theory, Performance Alignment Work, showing howSoftware developers experience performance.

Fabian Fagerholm, Marko Ikonen, Petri Kettunen et al. · 89 citations · ⚡5

What Can Software Startuppers Learn from the Artistic Design Flow? Experiences, Reflections and Future Avenues

This paper aims contributing to this gap by studying the artistic design flow and the tools utilized by architects, industrial designers and artists, and proposes concrete ways to improve the current state-of-practice.

Juhani Risku, P. Abrahamsson · 2 citations

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Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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