SSA-HIAST: A Novel Framework for Code Clone Detection

Abstract

In the recent era of software development, reusing software is one of the major activities that is widely used to save time. To reuse software, the copy and paste method is used and this whole process is known as code cloning. This activity leads to problems like difficulty in debugging, increase in time to debug and manage software code. In the literature, various algorithms have been developed to find out the clones but it takes too much time as well as more space to figure out the clones. Unfortunately, most of them are not scalable. This problem has been targeted upon in this paper. In the proposed framework, authors have proposed a new method of identifying clones that takes lesser time to find out clones as compared with many popular code clone detection algorithms. The proposed framework has also addressed one of the key issues in code clone detection i.e., detection of near-miss (Type-3) and semantic clones (Type-4) with significant accuracy of 95.52% and 92.80% respectively. The present study is divided into two phases, the first method converts any code into an intermediate representation form i.e., Hash-inspired abstract syntax trees. In the second phase, these abstract syntax trees are passed to a novel approach “Similarity-based self-adjusting hash inspired abstract syntax tree” algorithm that helps in knowing the similarity level of codes. The proposed method has shown a lot of improvement over the existing code clones identification methods.