TY - GEN
T1 - Similarity analysis of control software using graph mining
AU - Fahimipirehgalin, Mina
AU - Fischer, Juliane
AU - Bougouffa, Safa
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The control software of large scale industrial systems such as machines and plants in the domain of automated Production Systems (aPS) is oftentimes developed using the method clone and own. However, cloning is one of the reason for high maintenance cost in software lifecycle and thus, the need for clone detection is arising. In large scale control software, clone detection is a tedious work, which cannot be performed manually. In order to alleviate the problem of huge number of clones in control software, structural clone detection can be performed. Detecting structural clones can help in better understanding of large scale and complex software, detecting commonly-used design patterns, and software evolution. In this work, the software structure is represented as a call graph depicting software artefacts and their direct dependencies. These call graphs are compared based on graph mining approach to detect similarities between two software structures. The proposed method is adapted and applied to two industrial use cases with different size and complexity. The obtained similar fragments in the software structures are evaluated and verified through manual analysis. The results show that the proposed method is promising approach to capture the similarities between two software structures.
AB - The control software of large scale industrial systems such as machines and plants in the domain of automated Production Systems (aPS) is oftentimes developed using the method clone and own. However, cloning is one of the reason for high maintenance cost in software lifecycle and thus, the need for clone detection is arising. In large scale control software, clone detection is a tedious work, which cannot be performed manually. In order to alleviate the problem of huge number of clones in control software, structural clone detection can be performed. Detecting structural clones can help in better understanding of large scale and complex software, detecting commonly-used design patterns, and software evolution. In this work, the software structure is represented as a call graph depicting software artefacts and their direct dependencies. These call graphs are compared based on graph mining approach to detect similarities between two software structures. The proposed method is adapted and applied to two industrial use cases with different size and complexity. The obtained similar fragments in the software structures are evaluated and verified through manual analysis. The results show that the proposed method is promising approach to capture the similarities between two software structures.
KW - Automated production systems
KW - Graph mining
KW - PLC control code analysis
KW - Similarity detection
KW - Subgraph matching
UR - http://www.scopus.com/inward/record.url?scp=85079060936&partnerID=8YFLogxK
U2 - 10.1109/INDIN41052.2019.8972335
DO - 10.1109/INDIN41052.2019.8972335
M3 - Conference contribution
AN - SCOPUS:85079060936
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 508
EP - 515
BT - Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Y2 - 22 July 2019 through 25 July 2019
ER -