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Applying Text Analytics for Studying Research Trends in Dependability.

Miriam Louise Carnot1, Jorge Bernardino1,2, Nuno Laranjeiro2

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This study analyzes dependability research trends using NLP. Key findings reveal a strong focus on security, with emerging trends in machine learning and blockchain technologies.

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Area of Science:

  • Computer Science
  • Information Technology
  • Network Engineering

Background:

  • Dependability research has evolved significantly since the 1970s, with conferences like DSN becoming central to the field.
  • Dependability, initially focused on niche systems, is now crucial for most computer systems.
  • Analyzing research trends helps understand the field's evolution and emerging areas.

Purpose of the Study:

  • To analyze research trends in dependability using NLP techniques.
  • To identify active, collapsing, ephemeral, and new research lines in dependability conferences.
  • To examine the evolution of research topics within the dependability domain.

Main Methods:

  • Utilized Latent Dirichlet Allocation (LDA), a Natural Language Processing (NLP) algorithm.
  • Analyzed proceedings from major dependability conferences (DSN, ISSRE, SRDS, EDCC, LADC, PRDC).
  • Performed a case study on the Pacific Rim International Symposium on Dependable Computing (PRDC).

Main Results:

  • Identified a strong emphasis on 'security' terms across analyzed conferences.
  • Detected emerging research trends related to 'machine learning' and 'blockchain'.
  • The PRDC conference showed overall trends but also highlighted specific interests like 'cyber-physical' systems.

Conclusions:

  • NLP and LDA are effective tools for analyzing research trends in computer science fields.
  • The dependability research landscape shows a growing interest in security, machine learning, and blockchain.
  • Regional conferences like PRDC may exhibit unique research focuses alongside broader field trends.