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381 papers
Software engineering theory and practice is still to a large extent based more on faith than on science. Only by contributing to the scientific and empirically grounded body of knowledge within a specific area of application, theory and practice can develop. Experimentation is an important scientific approach to collec...
While extreme programming (XP) relies on certain principles, it requires an extensive set of tools to enable an effective execution of its practices. In many companies, putting stories on the board may not be sufficient for managing rapidly changing requirements. The objective of This work is to report the results from...
The analysis reveals that XP pays almost no attention on the usability of the software, apart from some actions that can be regarded as implicit usability evaluations, and that in XP the team transfers the responsibility of the product's usability to the customer.
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Empirical results show that with less than 4% effort it is possible to hold post-iteration workshops that significantly help to improve and optimize practices and enhance the learning and satisfaction of the project team.
A controlled case study approach is presented, which has been applied in a study of extreme programming methodology performed in close-to-industry settings, which considers the generation of both quantitative and qualitative data
Results from a study of a mobile application development project where the Offsite Customer Process Model was empirically evaluated show that the model itself is context-dependent and it has to be adapted to the underlying development process.
This paper suggests a test development process model that takes software reuse techniques and activities into account and shows further that in order to produce reusable test material, the software entities must be expressed in terms of features, in which the test materials are attached to.
This book constitutes the refereed research proceeding of the 12th European Software Process Improvement Conference, EuroSPI 2005, held in Budapest, Hungary in November 2005 and contains 18 revised full papers presented.
The empirical results offer support for the argument that the Naked Objects approach is suitable for agile software development and reveal weaknesses in the current Naked Object Framework.
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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