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T. Menzies, J. Greenwald, and A. Frank, “Data mining static code attributes to learn defect predictors,” IEEE Transactions on Software Engineering, January 2007, http://menzies.us/pdf/06learnPredict.pdf.

has been cited by the following article:

• TITLE: Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects

  AUTHORS: Tim MENZIES, Osamu MIZUNO, Yasunari TAKAGI, Tohru KIKUNO

  KEYWORDS: Explanation, Data Mining, Runaway

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.2 No.4, November 27, 2009

  ABSTRACT: Often, the explanatory power of a learned model must be traded off against model performance. In the case of predict-ing runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in predicting runaway projects. Measured in terms of precision, this new model is as good as can be expected for our data. Other methods might out-perform our result (e.g. by generating a smaller, more explainable model) but no other method could out-perform the precision of our learned model.