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human-computer interaction

2,089 papers

#computer vision May 2009

The Future of Lean in an Agile World

Questions that will be discussed and debated by this panel of leading practitioners and academics are: is Lean harder to adapt and adopt – does Lean require a greater critical mass to make value tangible?

S. Fraser, P. Abrahamsson, R. Davies et al. · 1 citation
#computer vision May 2009

Long-Term Effects of Test-Driven Development A Case Study

TDD has been found to improve the team confidence in the code quality and simplify significantly the software maintenance and is eager to continue improving the practice application.

A. Marchenko, P. Abrahamsson, T. Ihme · 30 citations · ⚡1
#computer vision May 2009

XP Workshop on Agile Product Line Engineering

Software Product Line Engineering (SPLE) promises to lower the costs of developing individual applications as they heavily reuse existing artifacts and, besides decreasing costs, software reuse achieves faster development and higher quality.

Yaser Ghanam, K. Cooper, P. Abrahamsson et al. · 1 citation

Positioning Agility

Nilay V. Oza, P. Abrahamsson, Kieran Conboy · 4 citations
#computer vision Review Jul 2009

Product-Focused Software Process Improvement, 10th International Conference, PROFES 2009, Oulu, Finland, June 15-17, 2009. Proceedings

The 34 papers presented together with 2 keynote speeches were carefully reviewed and selected and the topics covered are evidence-based software engineering and quality assurance, agile software development, process models and SPI, processes, software as a business.

F. Bomarius, M. Oivo, Pivi Jaring et al. · 3 citations
#computer vision Conference Sep 2010

Exploring the Sources of Waste in Kanban Software Development Projects

The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...

Marko Ikonen, Petri Kettunen, Nilay V. Oza et al. · 67 citations · ⚡9
#computer vision Jun 2010

The set-up of a software engineering research infrastructure of the 2010s

The Software Factory of today is not intellectually following the earlier undertakings with the same name stemming back in 1980s in Japan or in the US, but represents the software development of the 2010s.

P. Abrahamsson, Petri Kettunen, Fabian Fagerholm · 21 citations · ⚡1
#computer vision Review Jul 2011

Operationalizing the Concept of Success in Software Engineering Projects

An indicative project success model for software engineering projects is proposed and evaluated empirically in a large multinational software corporation setting, showing the model provides a valuable tool for KIBS organizations to increase their capabilities in running successful projects as well as to find targets fo...

Marko Ikonen, P. Abrahamsson · 6 citations

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Microsoft Research Blog Nov 5, 2026

Formally verifying agent-written full-stack web apps with PDD Kit

AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…

MIT News · Artificial Intelligence Oct 28, 2026

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

MIT News · Artificial Intelligence Oct 23, 2026

Reinforcing the value of human understanding at MIT

During the School of Humanities, Arts, and Social Sciences’ 75th Anniversary Symposium, President Sally Kornbluth said “MIT has never needed your particular insight and guidance more than we do now.”

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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