Open AccessDetection of Syntax Similarity of Source Code using a Graph based Hybrid Technique
Author(s) -
Babita Pathik,
Meera Sharma
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a4112.029420
Subject(s) - computer science , python (programming language) , dependency graph , source code , call graph , graph , programming language , abstract syntax , dependency (uml) , static program analysis , software , theoretical computer science , control flow graph , abstract syntax tree , syntax , data mining , software development , artificial intelligence
Software evolves inherently due to change requirement. The change request applied with intent to achieve the appropriate functionality of the software. This change inside the code makes some differences in previous code. Changes in somewhere in existing code may also affect some other part of the code. Our focus is on finding similarity of two codes to draw static call graph and program dependency graph which shows the dependencies and data flow among various part of the code then apply a distance metrics to find the percentage of similarity between two codes. This paper presents a dependency graph based hybrid technique (DGHT) for detection of similarity of two variations of python code. This method also includes a Machine learning technique which analyzes syntactic structure of object oriented software system. The objective is to apply the outcomes of this work on change impact analysis. The results of the framework will help to estimate actual impact set to optimize testing efforts.