Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling


IEEE ACCESS, vol.7, pp.82541-82552, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 7
  • Publication Date: 2019
  • Doi Number: 10.1109/access.2019.2924075
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.82541-82552
  • Karadeniz Technical University Affiliated: Yes


Software engineering is a data-driven discipline and an integral part of data science. The introduction of big data systems has led to a great transformation in the architecture, methodologies, knowledge domains, and skills related to software engineering. Accordingly, education programs are now required to adapt themselves to up-to-date developments by first identifying the competencies concerning big data software engineering to meet the industrial needs and follow the latest trends. This paper aims to reveal the knowledge domains and skill sets required for big data software engineering and develop a taxonomy by mapping these competencies. A semi-automatic methodology is proposed for the semantic analysis of the textual contents of online job advertisements related to big data software engineering. This methodology uses the latent Dirichlet allocation (LDA), a probabilistic topic-modeling technique to discover the hidden semantic structures from a given textual corpus. The output of this paper is a systematic competency map comprising the essential knowledge domains, skills, and tools for big data software engineering. The findings of this paper are expected to help evaluate and improve IT professionals' vocational knowledge and skills, identify professional roles and competencies in personnel recruitment processes of companies, and meet the skill requirements of the industry through software engineering education programs. Additionally, the proposed model can be extended to blogs, social networks, forums, and other online communities to allow automatic identification of emerging trends and generate contextual tags.