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2021 | OriginalPaper | Chapter

Tackling the Imbalanced Data in Software Maintainability Prediction Using Ensembles for Class Imbalance Problem

Authors : Ruchika Malhotra, Kusum Lata

Published in: Advances in Interdisciplinary Research in Engineering and Business Management

Publisher: Springer Nature Singapore

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Abstract

Early prediction of software maintainability is a potential solution to save the cost of software maintenance which is almost 60–70% of the software development cost. Various machine learning (ML) techniques have been investigated in literature to predict software maintainability. The performance of well-established ML techniques degrades considerably if the training dataset contains irregularities in the form of data skewness or class imbalance. This study empirically investigates the performance of two types of ensembles for class problem, namely Bagging-based ensembles and Boosting-based ensembles. The study also compares the predictive performance of proposed ensembles with classic ensembles: Bagging and AdaBoost. The results of the empirical analysis favor the use of proposed ensembles to develop effective Software Maintainability Prediction (SMP) models from imbalanced datasets.

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Metadata
Title
Tackling the Imbalanced Data in Software Maintainability Prediction Using Ensembles for Class Imbalance Problem
Authors
Ruchika Malhotra
Kusum Lata
Copyright Year
2021
Publisher
Springer Nature Singapore
Book
Advances in Interdisciplinary Research in Engineering and Business Management

Print ISBN: 978-981-16-0036-4

Electronic ISBN: 978-981-16-0037-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-0037-1_31