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381 papers
A model of desirable JSF factors is offered that contributes to the body of knowledge related to the decision-making process when selecting a JSF and is useful for Web and JSF developers.
A model of desirable JavaScript framework adoption factors is offered that contributes to the body of knowledge related to the adoption of technology by software engineers and is useful for decision makers when evaluating JavaScript frameworks, as well as for developers for producing desirable frameworks.
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Advertise in front of researchers, engineers, and readers.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
The preliminary finding results in a list of 17 security challenges with regards to technical, organizational and methodological perspectives, which provides initial explanation about the less emphasis on methodological and organizational security concerns in cases.
It is found that agile development is known to hardware entrepreneurs, however it is limitedly adopted, and four categories of tactics are found that enable hardware startups agile in their early stage business and product development.
This report documents the program and the outcomes of Dagstuhl Seminar 18182 "Software Business, Platforms, and Ecosystems: Fundamentals of Software Production Research".
This paper presents Essencery, a tool for essentializing software engineering methods and practices using the Essence graphical syntax and presents an empirical evaluation of the tool by means of a qualitative, quasi-formal experiment, which confirms that the tool is easy to use and useful for its intended purpose.
This paper studies Essence in an educational setting to evaluate its usefulness for software engineering students while also investigating barriers to its adoption in this context.
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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