Designing and Developing a Machine Learning Based Code Smell Detection Technique

Abstract

Software systems have become prevalent and signiand#64257;cant in our today s society. These systems are becoming the core business of several industrial companies and, for this reason, these systems are getting bigger and more complex. In addition, these systems are subject to frantic modiand#64257;cations every day with respect to the introduction of new functionality or bug and#64257;xing operations. In this sense, developers also do not have the ability to design and execute ideal solutions, contributing to quotcode smellsquot being introduced. Code smells refer to bad design and development practices commonly observed in software system. These smells reand#64258;ect the sub-optimal design choices applied in the source code by developers. Code smells are the symptoms that indicate problems in the coding part of software which makes software hard to change and maintain. Several studies demonstrated the negative impact of code smells on the maintainability of software as well as on the ability of developers to comprehend a software system. That is why, several automated techniques and tools have been devised to discover parts of code affected by design and#64258;aws in order to improve their quality. Most of these techniques rely on the analysis of the structural properties (e.g., method calls) mined from the source code. Despite the efforts of academicians and practitioners in recent years, there are still limitations that threaten the industrial applicability of techniques and tools for code smell identiand#64257;cation. Speciand#64257;cally, there is a lack of evidence regarding the circumstances that lead to the introduction of code smells and the real effect of code smells on maintainability, since previous research focused the attention on a small number of software projects. Furthermore, in literature, the existing code smell detectors might be inadequate for detecting many code smells. One reason for inadequacy includes the dependence of existing techniques on only the structural properties of software systems.