The Impact of Code Readability on Software Maintenance efficiency in open source development

The effectiveness of software maintenance is greatly influenced by code readability, especially in open-source development, where several developers work together on lengthy projects. Code readability's effects on software debugging effectiveness, refactoring success, feature enhancement speed, developer collaboration, issue resolution rate, and long-term maintainability are investigated in this study. A Multiple regression analysis was used in a quantitative research technique to assess the connection between software sustainability and readability. According to the results, code readability helps to explain 58.2% of the variance (R2 = 0.582, p <.001) in software maintenance efficiency by improving debugging efficiency (β = 0.523, p <.001), refactoring success (β = 0.498, p <.001), and feature enhancement speed (β = 0.467, p <.001). Additionally, 60.5% of the variance (R2 = 0.605, p <.001) in software sustainability was explained by positive effects on developer cooperation (β = 0.534, p <.001), problem resolution rate (β = 0.502, p <.001), and long-term maintainability (β = 0.478, p <.001). These results highlight the need to improve the quality of open-source software by using standardized coding procedures, automated readability tools, and collaborative development methodologies. To maximize software maintainability and long-term usability, the research suggests incorporating readability measures into software assessment frameworks, upholding coding standards, and encouraging peer review procedures.

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Publication Details

Journal
The Asian Bulletin of Big Data Management
Published
2025-03-05
DOI
https://doi.org/10.62019/abbdm.v5i1.300
Primary Topic
Software Reliability and Analysis Research
Type
article
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article

The Impact of Code Readability on Software Maintenance efficiency in open source development

S. Tauseef Ur Rehman, Salman Khan, Hafiz Muhammad Imran, Rooh ul Hasnain et al.
The Asian Bulletin of Big Data Management
Software Reliability and Analysis Research
article

The Impact of Code Readability on Software Maintenance efficiency in open source development

S. Tauseef Ur Rehman, Salman Khan, Hafiz Muhammad Imran, Rooh ul Hasnain, Muzamil Hussain AL Hussaini
article en

Abstract

The effectiveness of software maintenance is greatly influenced by code readability, especially in open-source development, where several developers work together on lengthy projects. Code readability's effects on software debugging effectiveness, refactoring success, feature enhancement speed, developer collaboration, issue resolution rate, and long-term maintainability are investigated in this study. A Multiple regression analysis was used in a quantitative research technique to assess the connection between software sustainability and readability. According to the results, code readability helps to explain 58.2% of the variance (R2 = 0.582, p <.001) in software maintenance efficiency by improving debugging efficiency (β = 0.523, p <.001), refactoring success (β = 0.498, p <.001), and feature enhancement speed (β = 0.467, p <.001). Additionally, 60.5% of the variance (R2 = 0.605, p <.001) in software sustainability was explained by positive effects on developer cooperation (β = 0.534, p <.001), problem resolution rate (β = 0.502, p <.001), and long-term maintainability (β = 0.478, p <.001). These results highlight the need to improve the quality of open-source software by using standardized coding procedures, automated readability tools, and collaborative development methodologies. To maximize software maintainability and long-term usability, the research suggests incorporating readability measures into software assessment frameworks, upholding coding standards, and encouraging peer review procedures.

The Asian Bulletin of Big Data ManagementVol. 5(1)
Sindh Madressatul Islam University (PK), Nanjing Normal University (CN), University of Sargodha (PK), Iqra University (PK), University of Education (PK)
Openalex Percentile: Top 95%
Software Reliability and Analysis Research
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