Code clone genealogy detection on e-health system using Hadoop. (July 2017)

• Record Type: Journal Article
• Authors: Tekchandani, Rajkumar; Bhatia, Rajesh; Singh, Maninder
• Is Part Of: Computers & electrical engineering. Volume 61(2017)
• Page Start: 15
• Page End: 30
• Publication Date: 2017-07
• Subjects: Code clone genealogy -- Transitive closure -- Clone group -- e-health -- Hadoop ecosystem; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.05.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Code clone genealogy is evolved over time along with each commit made by user on Git. A novel Git code clone genealogy extraction model is proposed on e-health system. Proposed model extracts code clone genealogy through transitive closure computation. The results prove that transitive pairs at each transitive depth are potential clone pairs. Abstract: Code clone genealogy detection has been widely used in refactoring and maintenance of code in software systems. It identifies history of code clones in a source code repository. Existing code clone genealogy detection techniques are based on text similarity for extraction of different versions of program. However, existing proposals in literature for code clone genealogy detection fail to extract code clone genealogy on Git (a distributed version control system). To address this issue, we presented a new scheme, based on transitive closure computation that detects code clone genealogies in e-health system on Git. Our insight is to extract clone pairs from thousands of commits on a software system in Git by transitive closure computation. We implemented the proposed scheme using Hadoop and Neo4j on Open Medical Record System. The results prove efficacy of the proposed scheme using parameters such as transitive depth, ratio of similarity, and count of clones. Graphical abstract: