Skip to content

Category

climate science

381 papers

An iterative improvement process for agile software development

An Iterative Improvement Process for conducting SPI within individual agile project teams is proposed, which aims at increasing the ability of software developers to improve the development process based on their experiences and context knowledge.

O. Salo, P. Abrahamsson · 81 citations · ⚡5
#computer vision Review Sep 2007

Software Process Improvement - EuroSPI 2007 Conference

This book constitutes the refereed research proceeding of the 14th European Software Process Improvement Conference, EuroSPI 2007, held in Potsdam, Germany in September 2007 and contains 18 revised full papers presented.

P. Abrahamsson, N. Baddoo, T. Margaria et al. · 0 citations
#computer vision Jun 2008

Culture and Agile: Challenges and Synergies

This panel brings together community experts to share and discuss research and field experience that can ameliorate cultural challenges to create synergies in Agile software practices.

S. Fraser, P. Abrahamsson, R. Biddle et al. · 3 citations
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#computer vision Conference Aug 2008

Scrum in a Multiproject Environment: An Ethnographically-Inspired Case Study on the Adoption Challenges

Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoptio...

A. Marchenko, P. Abrahamsson · 59 citations · ⚡11
#computer vision Conference Aug 2008

A Preliminary Roadmap for Empirical Research on Agile Software Development

Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on e...

Torgeir Dingsøyr, T. Dybå, P. Abrahamsson · 92 citations · ⚡7
#computer vision Review Mar 2008

Agile methods in European embedded software development organisations: a survey on the actual use and usefulness of Extreme Programming and Scrum

The results show that the embedded industry has been able to apply agile methods in its development processes and that the appreciation of the agile methods and their individual practices appears to increase once adopted and applied in practice.

O. Salo, P. Abrahamsson · 238 citations · ⚡9
#computer vision May 2008

Does Test-Driven Development Improve the Program Code? Alarming Results from a Comparative Case Study

A comparative case study of five small scale software development projects where the effect of TDD on program design was studied using both traditional and package level metrics reveals that an unwanted side effect can be that some parts of the code may deteriorate.

Maria Siniaalto, P. Abrahamsson · 35 citations · ⚡2
#computer vision Jun 2008

Architecture-Centric Methods and Agile Approaches

There is a vital need for devising a research agenda for identifying and dealing with architecture-centric challenges in agile software development to make it possible to guide the future research on integrating Architecture-centric methods in agile approaches and give advice to the software industry on dealing with Ar...

M. Babar, P. Abrahamsson · 13 citations

From tech blogs

See all →
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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.