An Analytical Study of Code Smells

Abstract

Software development process involves developing, building and enhancing high-quality software for specific tasks and as a consequence generates considerable amount of data. This data can be managed in a systematic manner creating knowledge repositories that can be used to competitive advantage. Lesson's learned as part of the development process can also be part of the knowledge bank and can be used to advantage in subsequent projects by developers and software practitioners. Code smells are a group of symptoms which reveal that code is not good enough and requires some actions to have a cleansed code. Software metrics help to detect code smells while refactoring methods are used for removing them. Furthermore, various tools are applicable for detecting of code smells. A Code smell repository organizes all the available knowledge in the literature about code smells and related concepts. An analytical study of code smells is presented in this paper which extracts useful, actionable and indicative knowledge.

The Development of the Secure Quality Dataset (SQDS): Combining Security and Quality Measures Using Deep Machine Learning for Code Smell Detection

“…In [19] the researchers use a dendrogram to illustrate the hierarchical grouping based on the correlations between software metrics and code smells to determine how comparable code smells are. This offers a new approach to classifying code smells.…”