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Data Mining Static Code Attributes to Learn Defect Predictors

Tim Menzies, Jeremy Greenwald, Art Frank
2007 IEEE Transactions on Software Engineering  
For information on obtaining reprints of this article, please send e-mail to: tse@computer.org, and reference IEEECS Log Number TSE-0001-0106. 1.  ...  Also, contrary to prior pessimism, we show that such defect predictors are demonstrably useful and, on the data studied here, yield predictors with a mean probability of detection of 71 percent and mean  ...  Our conclusion is that, contrary to prior pessimism [21] , [22] , data mining static code attributes to learn defect predictors is useful.  ... 
doi:10.1109/tse.2007.256941 fatcat:rnyudvlthrejna4prmup6eqxzq

Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"

Hongyu Zhang, Xiuzhen Zhang
2007 IEEE Transactions on Software Engineering  
In this correspondence, we point out a discrepancy in a recent paper, "Data Mining Static Code Attributes to Learn Defect Predictors," that was published in this journal.  ...  INTRODUCTION IN the January 2007 issue of this journal, a paper titled "Data Mining Static Code Attributes to Learn Defect Predictors" [1] was published.  ...  based on (3).  ... 
doi:10.1109/tse.2007.70706 fatcat:c22s2lslazdmfm2pgq7b46ciru

Problems with Precision: A Response to "Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors'"

Tim Menzies, Alex Dekhtyar, Justin Distefano, Jeremy Greenwald
2007 IEEE Transactions on Software Engineering  
s paper "Data Mining Static Code Attributes to Learn Defect Predictors" [13] (hereafter, DMP) are "not satisfactory for practical purposes."  ...  This detector is imprecise-it often triggers on well-written functions with detailed comments.  ... 
doi:10.1109/tse.2007.70721 fatcat:zjrqsqtmunc6lj55wxropx5hja

A Deep Analysis of the Precision Formula for Imbalanced Class Distribution

Gabriel Kofi Armah, Guangchun Luo, Ke Qin
2014 International Journal of Machine Learning and Computing  
We applied a fixed true positive rate (TPR) of one (1), whiles the false positive rate (FPR) on the other hand ranged from 0.01 to 0.05 inclusive at an interval of 0.01 for our analysis .We used the proposed  ...  In the January, 2007 issue of the IEEE Transactions on Software Engineering Journal, a paper with the title "Data Mining Static Code Attributes to Learn Defect Predictors" [1] , was published.  ...  This triggered a comment paper by Zhang and Zhang [2] .The two metrics pd and pf attracted the comment from [2] .  ... 
doi:10.7763/ijmlc.2014.v4.447 fatcat:gngjzh3eezhtngm5nyhfpfeib4

Further thoughts on precision

D. Gray, D. Bowes, N. Davey, Yi Sun, B. Christianson
2011 15th Annual Conference on Evaluation & Assessment in Software Engineering (EASE 2011)   unpublished
In January 2007 the Menzies et al. paper 'Data Mining Static Code Attributes to Learn Defect Predictors' was published in the IEEE Transactions on Software Engineering (Menzies, Greenwald & Frank 2007  ...  The use of these metrics motivated a comments paper by Zhang and Zhang (Zhang & Zhang 2007) .  ... 
doi:10.1049/ic.2011.0016 fatcat:mzm6uoy2u5fc5anktoyk5nrfyu

Software Defect Prediction Using Static Code Metrics: Formulating a Methodology [article]

David Philip Harry Gray, UH Research Archive, UH Research Archive
2013
(Cited on pages 84, 87 and 90.) [Menzies 2007b] Tim Menzies, Jeremy Greenwald and Art Frank. Data Mining Static Code Attributes to Learn Defect Predictors.  ...  Problems with Precision: A Response to "Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors" '. IEEE Transactions on Software Engineering, vol. 33, pages 637-640, 2007.  ...  Appendix E Selected Papers Special Issue on Evaluation and Assessment in Software Engineering 2011: Reflections on the NASA MDP Data Sets  ... 
doi:10.18745/th.11067 fatcat:gvwyfsonojdexpzakxgglhtxqe