Searching for "Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"." – sorted by Relevance.
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Problems with Precision: A Response to “Comments on ‘Data Mining Static Code Attributes to Learn
- with Precision: A Response to “Comments on ‘Data Mining Static Code Attributes to Learn Defect Predictors’” Tim
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Data mining static code attributes to learn defect predictors
- to prior pessimism [21], [22], data mining static code attributes to learn defect predictors is useful
- Cited by 9 (3 self) – Add To MetaCart
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Coactive Learning for Distributed Data Mining
- 1998 Coactive Learning for Distributed Data Mining Abstract We introduce coactive learning as a
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of the Data Mining and Machine Learning
- Learning methods are usually developed to work in static data sets. Yet, real world data typically changes
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Data Mining of Printed-Circuit Board Defects
- _______________________________________________________________________________ Data Mining of Printed-Circuit Board Defects Andrew Kusiak and Christian Kurasek Abstract—This paper
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Machine Learning and Data Mining
- in Prague Series of Research Reports Report No: GL 157/02 Machine Learning and Data Mining Jiˇrí Palou
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Abstract Evaluating Static Analysis Defect Warnings On Production Software
- Abstract Evaluating Static Analysis Defect Warnings On Production Software Nathaniel Ayewah
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EQ-Mine: Predicting short-term defects for software evolution
- issues. For defect prediction we expect that data mining features from many different categories
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Lightweight defect localization for java
- code (0.3% of all classes or 0.8% of the entire code) in order to find the defect. (In comparison
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Types and Classes of Machine Learning and Data Mining
- Types and Classes of Machine Learning and Data Mining Lloyd Allison School of Computer Science
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