Code smells, as an indicator aimed at characterizing the presence of poor practices, design issues, or potential bugs in code, have become a major research focus in the field of software engineering in recent years.Earlier studies predominantly focused on defining new types of code smells and their detection. However, as time has progressed, significant changes have occurred in the distribution and frequency of code smells.To investigate the trends and reasons behind these changes, we conducted a tracking analysis of code smells in classic projects maintained in various programming languages in recent years.
In this project,We identified common code smells in software evolution based on multiple software metrics indicators, such as duplicate code,The identified results underwent data processing, including data cleaning and analysis, to reveal their changing trends in software evolution and uncover the patterns of code smell evolution over time.
we visualized the data through charts to illustrate the evolution patterns over time, comparing the visualized curves generated from different programming languages to analyze the reasons for the differences in code smells produced by different languages. We also conducted comparisons based on the evolution dimension of the same programming language to analyze the reasons for the differences and similarities in code smells at different time periods. Furthermore, we analyzed the reasons for the differences in code smell density based on project size and code smell distribution.
In the folder data, we provide the basic information about the bad smell information of the code in various open-source projects after filtering. In the folder code, we provide the code we have written to analyze the bad smell of the code and visualize it