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Technical Note Open Access July 10, 2024

Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction

Linda Susan Amos 1,* and Eng Tirivangani Magadza 1
1
School of Information Science & Technology (SIST)-Harare Institute of Technology, Zimbabwe
Publihed in: Current Research in Public Health (Volume 3, Issue 2, 2024)
Page(s): 54-62
DOI: 10.31586/ujcsc.2024.949
Received
March 28, 2024
Revised
May 19, 2024
Accepted
July 08, 2024
Published
July 10, 2024
Keywords
Severity; Fairness Code Smells; Source Code; Big Code; Big Software Projects
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2024. Published by Scientific Publications
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Cite This Article

APA Style
Amos, L. S. , & Magadza, E. T. (2024). Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction. Current Research in Public Health, 3(2), 54-62. https://doi.org/10.31586/ujcsc.2024.949
ACS Style
Amos, L. S. ; Magadza, E. T. Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction. Current Research in Public Health 2024 3(2), 54-62. https://doi.org/10.31586/ujcsc.2024.949
Chicago/Turabian Style
Amos, Linda Susan, and Eng Tirivangani Magadza. 2024. "Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction". Current Research in Public Health 3, no. 2: 54-62. https://doi.org/10.31586/ujcsc.2024.949
AMA Style
Amos LS, Magadza ET. Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction. Current Research in Public Health. 2024; 3(2):54-62. https://doi.org/10.31586/ujcsc.2024.949 
@Article{crph949,
AUTHOR = {Amos, Linda Susan and Magadza, Eng Tirivangani},
TITLE = {Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction},
JOURNAL = {Current Research in Public Health},
VOLUME = {3},
YEAR = {2024},
NUMBER = {2},
PAGES = {54-62},
URL = {https://www.scipublications.com/journal/index.php/UJCSC/article/view/949},
ISSN = {2831-5162},
DOI = {10.31586/ujcsc.2024.949},
ABSTRACT = {Maintenance of large-scale software is difficult due to large size and high complexity of code.80% of software development is on maintenance and the other 60% is on trying to understand the code. The severity of the code smells must be measured as well as fairness on it because it will help the developers especially in large scale source code projects. Code smell is not a bug in the system as it doesn’t prevent the program from functioning but it may increase the risk of software failure or performance slowdown. Therefore, this paper seeks to help developers with early prediction of severity of code smells and test the level of fairness on the predictions especially in large scale source code projects. Data is the collection of facts and observations in terms of events, it is continuously growing, getting denser and more varied by the minute across different disciplines or fields. Hence, Big Data emerged and is evolving rapidly, the various types of data being processed are huge, but no one has ever thought of where this data resides, we therefore noticed this data resides in software’s and the codebases of the software’s are increasingly growing that is the size of the modules, functionalities, the size of the classes etc. Since data is growing so rapidly it also mean the codebases of software’s or code are also growing as well. Therefore, this paper seeks to discuss the 5V’s of big data in the context of software code and how to optimize or manage the big code. When we talk of "Big Code for Big Software's," we are referring to the specific challenges and considerations involved in developing, managing, and maintaining of code in large-scale software systems.},
}
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AB  - Maintenance of large-scale software is difficult due to large size and high complexity of code.80% of software development is on maintenance and the other 60% is on trying to understand the code. The severity of the code smells must be measured as well as fairness on it because it will help the developers especially in large scale source code projects. Code smell is not a bug in the system as it doesn’t prevent the program from functioning but it may increase the risk of software failure or performance slowdown. Therefore, this paper seeks to help developers with early prediction of severity of code smells and test the level of fairness on the predictions especially in large scale source code projects. Data is the collection of facts and observations in terms of events, it is continuously growing, getting denser and more varied by the minute across different disciplines or fields. Hence, Big Data emerged and is evolving rapidly, the various types of data being processed are huge, but no one has ever thought of where this data resides, we therefore noticed this data resides in software’s and the codebases of the software’s are increasingly growing that is the size of the modules, functionalities, the size of the classes etc. Since data is growing so rapidly it also mean the codebases of software’s or code are also growing as well. Therefore, this paper seeks to discuss the 5V’s of big data in the context of software code and how to optimize or manage the big code. When we talk of "Big Code for Big Software's," we are referring to the specific challenges and considerations involved in developing, managing, and maintaining of code in large-scale software systems.
DO  - Achieving Maintainability, Readability & Understandability of Software Projects using Code Smell Prediction
TI  - 10.31586/ujcsc.2024.949
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