4.5 Article

Taxonomies in software engineering: A Systematic mapping study and a revised taxonomy development method

期刊

INFORMATION AND SOFTWARE TECHNOLOGY
卷 85, 期 -, 页码 43-59

出版社

ELSEVIER
DOI: 10.1016/j.infsof.2017.01.006

关键词

Taxonomy; Classification; Software engineering; Systematic mapping study

资金

  1. CNPq (National Counsel of Technological and Scientific Development, Brazil)
  2. UFPI (Federal University of Piaui, Brazil)
  3. INES (National Institute of Science and Technology for Software Engineering, Brazil)

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Context: Software Engineering (SE) is an evolving discipline with new subareas being continuously developed and added. To structure and better understand the SE body of knowledge, taxonomies have been proposed in all SE knowledge areas. Objective: The objective of this paper is to characterize the state-of-the-art research on SE taxonomies. Method: A systematic mapping study was conducted, based on 270 primary studies. Results: An increasing number of SE taxonomies have been published since 2000 in a broad range of venues, including the top SE journals and conferences. The majority of taxonomies can be grouped into the following SWEBOI(knowledge areas: construction (19.55%), design (19.55%), requirements (15.50%) and maintenance (11.81%). Illustration (45.76%) is the most frequently used approach for taxonomy validation. Hierarchy (53.14%) and faceted analysis (39.48%) are the most frequently used classification structures. Most taxonomies rely on qualitative procedures to classify subject matter instances, but in most cases (86.53%) these procedures are not described in sufficient detail. The majority of the taxonomies (97%) target unique subject matters and many taxonomy-papers are cited frequently. Most SE taxonomies are designed in an ad-hoc way. To address this issue, we have revised an existing method for developing taxonomies in a more systematic way. Conclusion: There is a strong interest in taxonomies in SE, but few taxonomies are extended or revised. Taxonomy design decisions regarding the used classification structures, procedures and descriptive bases are usually not well described and motivated. (C) 2017 The Authors. Published by Elsevier B.V.

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