Enhancement of generic code clone detection model for python application

Identical code fragments in different locations are recognized as code clones. There are four native terminologies of code clones concluded as Type-1, Type-2, Type-3 and Type-4. Code clones can be identified using various approaches and models. Generic Code Clone Detection (GCCD) model was created to detect all four terminologies of code clones through five processes. A prototype has been developed to detect code clones in Java programming language that starts with Pre-processing Transformation, Parameterization, Categorization and ends with the Match Detection process. Hence, this work targeted to enhance the prototype using a GCCD model to identify all clone types in Python language. Enhancements are done in the Pre-processing process and parameterization process of the GCCD model to fit the Python language criteria. Results are improved by finding the best constant value and suitable weightage according to Python language. Proposed enhancement results of the Python language clone detection in GCCD model imply that Public as the weightage indicator and def as the best constant value