TY - GEN
T1 - Scalable cloud based semantic code analysis to support continuous integration of industrial PLC code
AU - Bougouffa, Safa
AU - Diehm, Sebastian
AU - Schwarz, Michael
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/10
Y1 - 2017/11/10
N2 - During the lifecycle of automated production systems (aPS) additional functionalities and evolutions are realized. As a consequence, control software of aPS becomes highly complex and hard to maintain, which rises the need for evaluating and improving the quality of the control software. Tools for assessing and analyzing the quality of control software are rare and mostly lack accessible platforms that allow the use of analysis data by quality officers and stakeholders. Therefore, this paper presents a cloud platform for code-analysis, a developed tool for evaluating control software by means of Semantic Web technologies. The scalability of the cloud platform supports varied volumes of data and allows efficient use of the analysis data in a continuous integration system for trend analysis in combination with software quality model that can indicate the overall quality level of the control software.
AB - During the lifecycle of automated production systems (aPS) additional functionalities and evolutions are realized. As a consequence, control software of aPS becomes highly complex and hard to maintain, which rises the need for evaluating and improving the quality of the control software. Tools for assessing and analyzing the quality of control software are rare and mostly lack accessible platforms that allow the use of analysis data by quality officers and stakeholders. Therefore, this paper presents a cloud platform for code-analysis, a developed tool for evaluating control software by means of Semantic Web technologies. The scalability of the cloud platform supports varied volumes of data and allows efficient use of the analysis data in a continuous integration system for trend analysis in combination with software quality model that can indicate the overall quality level of the control software.
UR - http://www.scopus.com/inward/record.url?scp=85041224374&partnerID=8YFLogxK
U2 - 10.1109/INDIN.2017.8104843
DO - 10.1109/INDIN.2017.8104843
M3 - Conference contribution
AN - SCOPUS:85041224374
T3 - Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
SP - 621
EP - 627
BT - Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Industrial Informatics, INDIN 2017
Y2 - 24 July 2017 through 26 July 2017
ER -