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381 papers
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?
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.
This workshop discussed the challenges, the research questions and the tradeoffs that need to be addressed for an agile and SPLE integration to enjoy success.
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.
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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.
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...
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.
Preliminary insight into success factors related to project outcomes that can be found at the midpoint of the development projects are offered.
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...
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
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’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
General Science
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