The Inductive Software Engineering Manifesto: Principles for Industrial Data Mining – MALETS 2011

by Tim Menzies, Christian Bird, Thomas Zimmermann, Wolfram Schulte, Ekrem Kocaganeli

The practices of industrial and academic data mining are very different. These differences have significant implications for (a) how we manage industrial data mining projects; (b) the direction of academic studies in data mining; and (c) training programs for engineers who seek to use data miners in an industrial setting.

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Reference

Tim Menzies, Christian Bird, Thomas Zimmermann, Wolfram Schulte, Ekrem Kocaganeli. The Inductive Software Engineering Manifesto: Principles for Industrial Data Mining. In Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering (MALETS 2011), Lawrence, Kansas, USA, November 2011.

BibTeX Entry

@inproceedings{menzies-malets-2011,
    title = "The Inductive Software Engineering Manifesto: Principles for Industrial Data Mining",
    author = "Tim Menzies and Christian Bird and Thomas Zimmermann and Wolfram Schulte and Ekrem Kocaganeli",
    year = "2011",
    month = "November",
    booktitle = "Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering",
    location = "Lawrence, Kansas, USA",
}