SOFTWARE DEFECT PREDICTION VIA MACHINE LEARNING CLASSIFIERS
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Author:
SHAISTA AMIN
Citable URI :
https://vspace.vu.edu.pk/detail.aspx?id=378
Publisher :
Virtual University
Date Issued:
7/6/2020 12:00:00 AM
Abstract
To improve software reliability, software should be developed without defects. SDP models can
be employed to identify defective code sections at initial stage during the software development.
When defects are ascertained early, it helps the practitioners to prioritize the efforts for testing and
allocating more resources to defective modules. This results in improved software quality,
reliability, and efficiency. Despite the fact that the numbers of approaches have been used in the
past for SDP but most of them are not practically applicable. Manual feature selection is mostly
performed by majority of feature selection methods. Core aim of this research is to propose
iterative feature selection technique using Boruta (random forest) for SDP model. This research
proposes Two–step preprocessing using SMOTE and BORUTA. Support Vector Machine (SVM),
Neural Networks and XGboostclassifiers are used by MLC. Furthermore to confirm the accuracy,
performance and capability of each classifier on PROMISE dataset evaluationmeasures
AUC,recall, F1-measure, andaccuracy are used
URI :
https://vspace.vu.edu.pk/details.aspx?id=378
Citation:
AMIN,S(2019),SOFTWARE DEFECT PREDICTION VIA MACHINE LEARNING CLASSIFIERS,VIRTUAL UNIVERSITY OF PAKISTAN.(Lahore,Pakistan).
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Final Version
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