Category
275 papers
Reach audiences
Advertise in front of researchers, engineers, and readers.
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 adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
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 experienced agile teams and organizations, connecting better to existing streams of research in more established fields, giving more attention to management-oriented approaches, and finally give more emphasis to the core ideas in agile software development in order to increase our understanding. We hope that this preliminary roadmap serves as a starting point for creating a common research agenda and enables the generation of fruitful discussions and research results from the field.
Data Management
Health & Bioscience
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
Generative AI
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
