Prediction Model to Investigate Influence of Code Smells on Metrics in Apache Tomcat
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Abstract
For advancing software maintenance process, attempts are necessitated at developers end. One such endeavour is applying refactoring to eliminate code smells from the software. The aim of refactoring process is to identify the smelly areas known as Code Smells. It makes the code livelier, easier to read and hence understanding of code increases. The aim of the paper is to perform an empirical analysis on the code smells and metrics. A set of object oriented metrics are selected for the study. Hence the study introduces a metric based prediction model of code smells. The paper initially introduces the statistical relationship between code smells and metrics. Based on the results neural network model development is made possible. The accuracy of the developed model is validated on machine learning algorithms. Four versions of Apache Tomcat (6.0, 7.0, 8.0, 8.5.11) are selected for the work. Successive versions of Tomcat source code are applied for validation of study. The results from the study revealed that metrics can predict smelly classes effectively
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