An Empirical Study of the Correlation between Code Smells And Software Bugs

Code smells;Severity;Software bugs;Software engineering;Association with fault proneness;Computer science;Software Engineering, College of Engineering & Computer Science

Ganesan, GayathriGrosky, William ;

University of Michigan

关键词: Code smells;    Severity;    Software bugs;    Software engineering;    Association with fault proneness;    Computer science;    Software Engineering, College of Engineering & Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/147432/Dec%2019-%20Thesis%20Report_GANESAN%20GAYATHRI_4pm_FontsEmbedded.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Bug predictions helps software quality assurance team to determine the effort required to test the software application. Anti-patterns and code smells can greatly influence the quality of the code. Refactoring can be a solution to remove the negative impact of these anti-patterns. In this paper, we explored the influence of code smells on the code and severity of bugs reported on multiple versions of the projects such as BIRT, Aspect J and SWT. We evaluated the correlation between the different code smells and severity of the bugs reported on these classes. This can help the quality assurance specialists and project managers assess the testing effort required based on the code smells detected. This can prove beneficial to the developers to restructure or refactor before deploying the code in the test environment. On the other hand, the testing team can concentrate on the bug prediction models, testing plan and assess the number of resources needed to perform testing. The empirical validation of our work found a strong correlation between several types of code smells and software bugs based on three large open source projects.