Abstract
Nowadays, the industry is undergoing the Fourth Industrial Revolution, and techniques and algorithms for automated compliance checking (ACC) are continually being improved, generalized and flexible with the use of semantic and logic-based representations. However, there is still a lack of research that addresses advances in ACC tools headed for the Industry 4.0 (I4.0) perspective and that point out practitioners’ perceptions concerning the current application of these technologies. In this sense, this paper aims to conduct a systematic review to identify the main automated code-checking tools to regulatory compliance in the I4.0 era, indicating new trends and possible gaps, and then ranking the selected technologies with the support of two soft hybrid multi-criteria decision-making techniques. By content analysis, we identified and categorized seven main technologies used to ACC in several industrial areas. As a practical example of group decision-making and modeling of uncertainty, we implemented fuzzy analytical hierarchical process to aggregate different perspectives and obtain the criteria weights and combined it with TOPSIS and VIKOR methods. To do this, by means of expert panel, we evaluated practitioner’s perception about the surveyed technologies concerning the ISO/IEC 9126 standard. The results indicate that both methods have shown similar rankings, and although portability was considered the most relevant criterion, open-source tools are key issues when choosing ACC technologies. Several further research trends were also pointed out through this theoretical–empirical approach, such as the use of natural language processing, declarative rule processing and flow-based processing.
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This study was funded by Agência Nacional do Petróleo, Gás Natural e Biocombustíveis—ANP (Grant Number 20288-7) and Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq (Grant Number 300007/2019-1).
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Cunha, V.H.C., Caiado, R.G.G., Corseuil, E.T. et al. Automated compliance checking in the context of Industry 4.0: from a systematic review to an empirical fuzzy multi-criteria approach. Soft Comput 25, 6055–6074 (2021). https://doi.org/10.1007/s00500-021-05599-3
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DOI: https://doi.org/10.1007/s00500-021-05599-3