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	Menzies, Tim     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending Date Reviewed     1 - 5 of 5 reviews        (Re)use of research results (is rampant) Baldassarre M., Ernst N., Hermann B., Menzies T., Yedida R. Communications of the ACM 66(2): 75-81, 2023.  Type: Article The point of this article is that scholars often make research contributions that do not fit neatly into the category of peer-reviewed journal papers, and that impacts the advance of knowledge negatively. First, the products of research simply do ...... Jul 18 2023     Perspectives on data science for software engineering Menzies T., Williams L., Zimmermann T., Morgan Kaufmann Publishers Inc., San Francisco, CA, 2016. 408 pp.  Type: Book (978-0-128042-06-9) Data science is a very hot topic. Case in point, we recently hired two specialist data scientists at my company. But data science is not something you pick up by running the latest data mining tool on your big data repository; rather, ...... Apr 13 2017     Balancing privacy and utility in cross-company defect prediction Peters F., Menzies T., Gong L., Zhang H. IEEE Transactions on Software Engineering 39(8): 1054-1068, 2013.  Type: Article Cross-company defect prediction (CCDP) uses quality assessment data from multiple companies to predict defects in software under development. The advantages of access to rich data sources outside one’s own company must be bal...... Oct 18 2013     The inductive software engineering manifesto: principles for industrial data mining Menzies T., Bird C., Zimmermann T., Schulte W., Kocaganeli E.  MALETS 2011 (Proceedings of the International Workshop on Machine Learning Technologies in Software Engineering, Lawrence, KS, Nov 12, 2011) 19-26, 2011.  Type: Proceedings Lessons learned from data mining projects in industry are presented in the form of seven principles and a dozen tips. No questionnaire or interview surveys were administered. The lessons learned are simply drawn from the authors...... Dec 18 2012     More success and failure factors in software reuse Menzies T., Di Stefano J. IEEE Transactions on Software Engineering 29(5): 474-477, 2003.  Type: Article This short note discusses some learning algorithms. It applies these algorithms to previously published data [1], which summarized the success or failure of 24 reuse projects....... Dec 1 2003
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