Categorizing software engineering knowledge using a combination of SWEBOK and text categorization

Authors:
Jianying He;Haihua Yan;Maozhong Jin;Chao Liu
Affiliations:
School of Computer Science & Engineering, Beihang University, Beijing, P.R. China;School of Computer Science & Engineering, Beihang University, Beijing, P.R. China;School of Computer Science & Engineering, Beihang University, Beijing, P.R. China;School of Computer Science & Engineering, Beihang University, Beijing, P.R. China
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

In this paper, we utilize a combination of SWEBOK and text categorization to categorize software engineering knowledge. SWEBOK serves as a backbone taxonomy while text categorization provides a collection of algorithms including knowledge representation, feature enrichment and machine learning. Firstly, fundamental knowledge types in software engineering are carefully analyzed as well as their characteristics. Then, incorporated with SWEBOK, we propose a knowledge categorization methodology as well as its implementing algorithms. Finally, we conduct experiments to evaluate the proposed method. The experimental results demonstrate that our methodology can serve as an effective solution for the categorization of software engineering knowledge.