research-article

Maintainability prediction of object-oriented software system by multilayer perceptron model

Authors:

Sanjay Kumar Dubey,

Yajnaseni DashAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 37, Issue 5

Pages 1 - 4

https://doi.org/10.1145/2347696.2347703

Published: 02 September 2012 Publication History

Abstract

To accomplish software quality, correct estimation of maintainability is essential. However there is a complex and non-linear relationship between object-oriented metrics and maintainability. Thus maintainability of object-oriented software can be predicted by applying sophisticated modeling techniques like artificial neural network. Multilayer Perceptron neural network is chosen for the present study because of its robustness and adaptability. This paper presents the prediction of maintainability by using a Multilayer Perceptron (MLP) model and compares the results of this investigation with other models described earlier. It is found that efficacy of MLP model is much better than both Ward and GRNN network models.

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Bombiri OPoda POuedraogo T(2023)Application of Machine Learning in Software Quality: a Mini-review2023 IEEE Multi-conference on Natural and Engineering Sciences for Sahel's Sustainable Development (MNE3SD)10.1109/MNE3SD57078.2023.10079800(1-7)Online publication date: 23-Feb-2023
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Index Terms

Maintainability prediction of object-oriented software system by multilayer perceptron model
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 37, Issue 5

September 2012

129 pages

ISSN:0163-5948

DOI:10.1145/2347696

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Copyright © 2012 Authors.

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Association for Computing Machinery

New York, NY, United States

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Published: 02 September 2012

Published in SIGSOFT Volume 37, Issue 5

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Cited By

Lv LZheng ZXu JFu HRen LYang PXie WYang D(2024)Research on trimming path for forked carrots using contour‐based machine learning methodsJournal of Food Process Engineering10.1111/jfpe.1452747:1Online publication date: 10-Jan-2024
https://doi.org/10.1111/jfpe.14527
Qi XZhu TLi Y(2024)Coverage-enhanced fault diagnosis for Deep Learning programs: A learning-based approach with hybrid metricsInformation and Software Technology10.1016/j.infsof.2024.107488173(107488)Online publication date: Sep-2024
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Gupta SChug A(2021)An Optimized Extreme Learning Machine Algorithm for Improving Software Maintainability Prediction2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence51648.2021.9377196(829-836)Online publication date: 28-Jan-2021
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Lakra KChug A(2021)Improving Software Maintainability Prediction Using Hyperparameter Tuning of Baseline Machine Learning AlgorithmsApplications of Artificial Intelligence and Machine Learning10.1007/978-981-16-3067-5_51(679-692)Online publication date: 27-Jul-2021
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