Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/29366
Title: Analysis of Industrial Control System Software to Detect Semantic Clones
Contributor(s): Jnanamurthy, H K (author); Raoul, Jetley (author); Henskens, Frans (author); Paul, David  (author)orcid ; Wallis, Mark (author); Sudarsan, S D (author)
Publication Date: 2019
DOI: 10.1109/ICIT.2019.8754957
Handle Link: https://hdl.handle.net/1959.11/29366
Abstract: The detection of software clones is gaining more attention due to the advantages it can bring to software maintenance. Clone detection helps in code optimization (code present in multiple locations can be updated and optimized once), bug detection (discovering bugs that are copied to various locations in the code), and analysis of re-used code in software systems. There are several approaches to detect clones at the code level, but existing methods do not address the issue of clone detection in the PLC-based IEC 61131-3 languages. In this paper, we present a novel approach to detect clones in PLC-based IEC 61131-3 software using semantic-based analysis. For the semantic analysis, we use I/O based dependency analysis to detect PLC program clones. Our approach is a semantic-based technique to identify clones, making it feasible even for large code bases. Further, experiments indicate that the proposed method is successful in identifying software clones.
Publication Type: Conference Publication
Conference Details: ICIT 2019: 20th IEEE International Conference on Industrial Technology, Melbourne, Australia, 13th - 15th February, 2019
Source of Publication: Proceedings of the 20th IEEE International Conference on Industrial Technology (ICIT), p. 773-779
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Place of Publication: Piscataway, United States of America
Fields of Research (FoR) 2008: 080309 Software Engineering
080109 Pattern Recognition and Data Mining
Fields of Research (FoR) 2020: 460207 Modelling and simulation
460104 Applications in physical sciences
Socio-Economic Objective (SEO) 2008: 890299 Computer Software and Services not elsewhere classified
Socio-Economic Objective (SEO) 2020: 220402 Applied computing
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Publisher/associated links: http://www.ieee-icit2019.org/index.php
https://ieeexplore.ieee.org/document/8754957/authors#authors
Appears in Collections:Conference Publication
School of Science and Technology

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