software defect prediction using regression via classification
Software Defect Prediction Using Regression via Classification. Bibi S., Ts...
Software Defect Prediction Using Regression via Classification. Bibi S., Tsoumakas G., Stamelos I., Vlahavas I. Department of Informatics, Aristotle University of.
⬇ Download Full VersionAbstract In this paper we apply a machine,learning approach to the problem,...
Abstract In this paper we apply a machine,learning approach to the problem,of estimating the number,of defects called Regression,via,Classification (RvC).
⬇ Download Full VersionSoftware Defect Prediction Using Regression via Classification. Published i...
Software Defect Prediction Using Regression via Classification. Published in: Computer Systems and Applications, IEEE International Conference on.
⬇ Download Full VersionSoftware Defect Prediction Using Regression via Classification, Article. Bi...
Software Defect Prediction Using Regression via Classification, Article. Bibliometrics Data Bibliometrics. · Downloads (6 Weeks): 0.
⬇ Download Full VersionWe use several classification algorithms for the implemen- tation of the Rv...
We use several classification algorithms for the implemen- tation of the RvC framework and we assess their efficiency for the task of software defect prediction.
⬇ Download Full VersionIn this paper we apply a machine learning approach to the problem of estima...
In this paper we apply a machine learning approach to the problem of estimating the number of defects called Regression via Classification (RvC). RvC initially.
⬇ Download Full Versionfor different data set. Keywords: Software defect prediction, classificatio...
for different data set. Keywords: Software defect prediction, classification Algorithm, Cofusion matrix. Regression via classification.
⬇ Download Full VersionSoftware Defect Prediction Using Regression via Classification. In Proceedi...
Software Defect Prediction Using Regression via Classification. In Proceedings of IEEE/ACS International Conference on Computer Systems and Applications.
⬇ Download Full VersionPredicting Systems Performance through Requirements Quality Attributes Mode...
Predicting Systems Performance through Requirements Quality Attributes Model I. Vlahavas, “Software Defect Prediction Using Regression via Classification.
⬇ Download Full VersionAlso Web based GIS platform architecture is discussed along with two I,” So...
Also Web based GIS platform architecture is discussed along with two I,” Software Defect Prediction by Using Regression via Classification” To solve a.
⬇ Download Full VersionSoftware metrics related studies mainly consist of time series prediction, ...
Software metrics related studies mainly consist of time series prediction, defect it is a promising technique through comparison with multivariate linear regression, regression via classification to estimate the number of software defects by.
⬇ Download Full VersionKEYWORDS. Software Quality Estimation, Software Metrics, Regression, Neural...
KEYWORDS. Software Quality Estimation, Software Metrics, Regression, Neural Networks, Fuzzy Logic Fenton and Neil [13] reviewed a wide range of defect prediction models, which mostly relied on The first sub-model used 21 defect introduction drivers together with the size of the project to derive via classification.
⬇ Download Full VersionSoftware Defect Prediction Using Regression via Classification. In IEEE Int...
Software Defect Prediction Using Regression via Classification. In IEEE International Conference on Computer Systems and Applications, (pp. –).
⬇ Download Full Versionclude that classifier ensembles with decision-making strategies not based o...
clude that classifier ensembles with decision-making strategies not based on majority voting . Many studies of software defect prediction have been performed over the years. . () report that Regression via Classification works well.
⬇ Download Full VersionContent Based Image Retrieval System with a Combination of Rough “Regressio...
Content Based Image Retrieval System with a Combination of Rough “Regression via Classification applied on software defect estimation,” Expert Cukic, and H. Singh, “Robust prediction of faultproneness by random forests,” in Software.
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