Predicting software maintainability in object-oriented systems using ensemble techniques

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Alsolai, Hadeel; (2018) Predicting software maintainability in object-oriented systems using ensemble techniques. In: 2018 IEEE International Conference on Software Maintenance and Evolution. IEEE, ESP, pp. 716-721. ISBN 9781538678701 (https://doi.org/10.1109/ICSME.2018.00088)

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Abstract

Prediction of the maintainability of classes in object-oriented systems is a significant factor for software success, however it is a challenging task to achieve. To date, several machine learning models have been applied with variable results and no clear indication of which techniques are more appropriate. With the goal of achieving more consistent results, this paper presents the first set of results in an extensive empirical study designed to evaluate the capability of bagging models to increase accuracy prediction over individual models. The study compares two major machine learning based approaches for predicting software maintainability: individual models (regression tree, multilayer perceptron, k-nearest neighbors and m5rules), and an ensemble model (bagging) that are applied to the QUES data set. The results obtained from this study indicate that k-nearest neighbors model outperformed all other individual models. The bagging ensemble model improved accuracy prediction significantly over almost all individual models, and the bagging ensemble models with k-nearest neighbors as a base model achieved superior accurate prediction. This paper also provides a description of the planned programme of research which aims to investigate the performance over various datasets of advanced (ensemble-based) machine learning models.

  • Item type:Book Section
    ID code:67014
    Dates:
    Date
    Event
    12 November 2018
    Published
    5 June 2018
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Computer software
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:19 Feb 2019 12:58
    Last modified:17 Nov 2024 01:30
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/67014
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