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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2016 Vol.4(2): 107-115 ISSN: 2301-3559
DOI: 10.7763/LNSE.2016.V4.234

Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor

Gabriel Kofi Armah, Guanchun Luo, Ke Qin, and Angolo Shem Mbandu
Abstract—Empirical studies on software defect prediction models have come up with various predictors. In this study we examined variable regularized factors in conjunction with Logistic regression. Our work was built on eight public NASA datasets commonly used in this field. We used one of the datasets for our learning classification out of which we selected the regularization factor with the best predictor model; we then used the same regularization factor to classify the other seven datasets. Our proposed algorithm Variant Variable Regularized Logistic Regression (VVRLR) and modified VVRLR; were then used in the following metrics to measure the effectiveness of our predictor model: accuracy, precision, recall and F-Measure for each dataset. We measured above metrics using three Weka models, namely: BayesianLogisticRegression, NaiveBayes and Simple Logistic and then compared these results with VVRLR. VRLR and modified VVRLR outperformed the weka algorithms per our metric measurements. The VVRLR produced the best accuracy of 100.00%, and an average accuracy of 91.65 %; we had an individual highest precision of 100.00%, highest individual recall of 100.00% and F-measure of 100.00% as the overall best with an average value of 76.41% was recorded by VVRLR for some datasets used in our experiments. Our proposed modified VVRLR and variant VVRLR algorithms for F-measures outperformed the three weka algorithms.

Index Terms—F-measure, precision, recall, variant variable regularized logistic regression.

G. K. Armah is with University of Electronic Science of China (UESTC), China. He is also with University for Development Studies, Navrongo, Ghana (e-mail: gabrielarmah1@com).
G. Luo and Ke Qin are with School of Computer Science and Engineering, University of Electronic Science and Technology of China/Computer Science, Chengdu, China (e-mail: gcluo@uestc.edu.cn, qinke@uestc.edu.cn).
Angolo Shem Mbandu is with UESTC/Computer Science, China.


Cite: Gabriel Kofi Armah, Guanchun Luo, Ke Qin, and Angolo Shem Mbandu, "Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor," Lecture Notes on Software Engineering vol. 4, no. 2, pp. 107-115, 2016.

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