AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: LIVIU-MARIAN BERCIU.
DOI: 10.24193/subbi.2023.2.02
Published Online: 2023-12-22
pp. 22-40
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Software maintainability is a crucial factor impacting cost, time and resource allocation for software development. Code refactorings greatly enhance code quality, readability, understandability and extensibility. Hence, accurate prediction methods for both maintainability and refactorings are vital for long-term project sustainability and success, offering substantial benefits to the software community as a whole. This article focuses on prediction of software maintainability and the number of needed code refactorings using technical debt data. Two approaches were explored, one compressing technical debt issues per software component and employing machine learning algorithms such as ExtraTrees, Random Forest, Decision Trees, which all obtained a high accuracy and performance. The second approach retained multiple debt issue entries and utilized a Recurrent Neural Network, although less effectively. In addition to the prediction of the requisite number of code refactorings and software maintainability for individual software components, a comprehensive analysis of technical debt issues was conducted before and after the refactoring process. The outcomes of this study contribute to the advancement of a dependable prediction system for maintainability and refactorings, presenting potential advantages to the software community in effectively managing maintenance resources. Of all the employed models, the ExtraTrees model yielded the most optimal predictive outcomes. To the best of our knowledge no other approaches of using ML techniques for this problem have been reported in the literature.
Received by the editors: 19 June 2023.
2010 Mathematics Subject Classification. 68N99.
1998 CR Categories and Descriptors. D2.0 [Software Engineering]: General – Stan- dards; D2.9 [Software Engineering]: Management – Software Quality Assurance.
Keywords and phrases: Software Quality, Sonarqube, Refactoring, Code Smells.
