Applying Text Analytics for Studying Research Trends in Dependability

• Autor(a) principal: Carnot, Miriam Louise
• Data de Publicação: 2020
• Outros Autores: Bernardino, Jorge, Laranjeiro, Nuno, Oliveira, Hugo Gonçalo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/106025; https://doi.org/10.3390/e22111303
• topic: topic modeling; dependability; LDA; text analytics

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability-The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)-emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like 'security', despite the general focus of the conferences in dependability and new trends that are related with 'machine learning' and 'blockchain'. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like 'cyber-physical', being popular at PRDC and not in the overall dataset.