Data In Brief _Code Smells_Metrics_Fault_ Data_TOMCAT

Published: 18 July 2022| Version 1 | DOI: 10.17632/8mpjtrjh45.1
Sharanpreet Kaur,


For the development of prediction model the association between metrics, code smells and faulty classes in post release object oriented open source systems (Tomcat) is examined. An array of metrics is used as independent variables which includes diverse characteristics of design. Two type of cataloging for code smells - Class Level and Method Level are performed with a set of eight code smells as dependent variables. Reverse engineering code smell predictor application - iPlasma was used which was able to detect the selected code smells and Object Oriented metrics. The perceptiveness of the model on the whole is considered to be of fair to good quality and the model qualifies to be called as a successful model which may require further performance tuning in terms of data and algorithm parameters.



Software Maintenance, Code Refactoring, Software Quality Assurance