Classification and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning Techniques

Anshita Malviya

2021

Abstract

In software engineering, maintenance is the one of the most crucial, costly and difficult activity. Numerous research works are still going on in this area to reduce and measure the maintenance cost. The maintenance consumes up to 80% of the total software development cost. There is a trend of developing software using object oriented techniques due to obvious reasons. In this paper, we proposed a classification model to identify high and low maintainable class at design level of Object Oriented Software development process. This model is implemented in python using Machine Learning Techniques. Experiment is simulated on Jupiter Notebook.

Paper Citation

in Harvard Style

Malviya A. (2021). Classification and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning Techniques. In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE, ISBN 978-989-758-544-9, pages 170-176. DOI: 10.5220/0010564700003161

in Bibtex Style

@conference{icacse21,
author={Anshita Malviya},
title={Classification and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning Techniques},
booktitle={Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,},
year={2021},
pages={170-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010564700003161},
isbn={978-989-758-544-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,
TI - Classification and Prediction of High and Low Maintainable Class of Object Oriented Systems at Design Level using Machine Learning Techniques
SN - 978-989-758-544-9
AU - Malviya A.
PY - 2021
SP - 170
EP - 176
DO - 10.5220/0010564700003161