Machine Learning-Based Exploration of the Impact of Move Method Refactoring on Object-Oriented Software Quality Attributes

• Published in: Arabian journal for science and engineering (2011) Vol. 49; no. 3; pp. 3867 - 3885
• Main Authors: Al Dallal, Jehad, Abdulsalam, Hanady, AlMarzouq, Mohammad, Selamat, Ali
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2024; Springer Nature B.V
• Subjects: Engineering; Humanities and Social Sciences; Machine learning; multidisciplinary; Prediction models; Quality management; Research Article-Computer Engineering and Computer Science; Science; Software; Software quality; Source code; Class coupling; Logistic regression analysis; Software metrics; Object-oriented software; Class size; Machine learning; Class cohesion; Internal quality attributes; Classification tree; Move method refactoring; Quality measures
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-023-08174-0

Refactoring is a maintenance task that aims at enhancing the quality of a software’s source code by restructuring it without affecting the external behavior. Move method refactoring (MMR) involves reallocating a method by moving it from one class to the class in which the method is used most. Several studies have been performed to explore the impact of MMR on several quality attributes. However, these studies have several limitations related to the applied approaches, considered quality attributes, and size of the selected datasets. This paper reports an empirical study that applies statistical and machine learning (ML) approaches to explore the impact of MMR on code quality. The study overcame the limitations of the existing studies, and this improvement is expected to make the results of this study more reliable and trustworthy. We considered eight quality attributes and thirty quality measures, and a total of approximately 4 K classes from seven Java open-source systems were involved in the study. The results provide evidence that most of the quality attributes were significantly improved by MMR in most cases. In addition, the results show that a limited number of measures, when considered individually, have a significant ability to predict MMR, whereas most of the considered measures, when considered together, significantly contribute to the MMR prediction model. The constructed ML-based prediction model has an area under curve (AUC) value of 96.6%.