research-article

Supporting architecture documentation: a comparison of two ontologies for knowledge retrieval

Authors:

Klaas Andries de Graaf,

Hans van VlietAuthors Info & Claims

EASE '15: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering

Article No.: 3, Pages 1 - 10

https://doi.org/10.1145/2745802.2745804

Published: 27 April 2015 Publication History

Abstract

Context: Software architecture documentation is used to communicate architectural knowledge. It is often difficult for document users to find all the architectural knowledge they need to do their tasks, and this results in wasted time and mistakes during development. Objective: In this paper we investigate how ontology-based documentation may support users in finding the architectural knowledge they need. Method: We executed a controlled experiment to test for differences in knowledge retrieval efficiency and effectiveness between two groups of master students that used two ontologies built from different understandings of the architectural knowledge needs of document users. Results: Use of the ontology built based on a better understanding of architectural knowledge needs was significantly more efficient or effective for retrieving part of the knowledge needed by document users. We analysed participants' search actions and identified which organisation of knowledge in the ontologies resulted in efficient and effective knowledge retrieval. Conclusion: We found that an improved understanding of knowledge needs allows for the construction of an ontology from which document users retrieve knowledge more efficiently and effectively. In some cases we found that the ontology support for knowledge needs had to be traded off against ontology design criteria.

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Cited By

Amiri ANtentos EZdun UGeiger S(2023)Tool Support for Learning Architectural Guidance Models from Architectural Design Decision ModelsProceedings of the 28th European Conference on Pattern Languages of Programs10.1145/3628034.3628037(1-14)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3628034.3628037
Graaf KTang ALiang PKhalili A(2017)Querying Software Architecture Knowledge as Linked Open Data2017 IEEE International Conference on Software Architecture Workshops (ICSAW)10.1109/ICSAW.2017.13(272-277)Online publication date: Apr-2017
https://doi.org/10.1109/ICSAW.2017.13

Index Terms

Supporting architecture documentation: a comparison of two ontologies for knowledge retrieval
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Documentation

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cover image ACM Other conferences

EASE '15: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering

April 2015

305 pages

ISBN:9781450333504

DOI:10.1145/2745802

General Chair:
Jian Lv
Nanjing University, China
,
Program Chairs:
He (Jason) Zhang
Nanjing University, China
,
Muhammad Ali Babar
University of Adelaide, Australia

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NJU: Nanjing University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 April 2015

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Funding Sources

Natural Science Foundation of China (NSFC)
Regeling Kenniswerkers (The Netherlands)

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EASE '15

Sponsor:

NJU

EASE '15: 19th International Conference on Evaluation and Assessment in Software Engineering

April 27 - 29, 2015

Nanjing, China

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EASE '15 Paper Acceptance Rate 20 of 65 submissions, 31%;

Overall Acceptance Rate 71 of 232 submissions, 31%

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Cited By

Amiri ANtentos EZdun UGeiger S(2023)Tool Support for Learning Architectural Guidance Models from Architectural Design Decision ModelsProceedings of the 28th European Conference on Pattern Languages of Programs10.1145/3628034.3628037(1-14)Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1145/3628034.3628037
Graaf KTang ALiang PKhalili A(2017)Querying Software Architecture Knowledge as Linked Open Data2017 IEEE International Conference on Software Architecture Workshops (ICSAW)10.1109/ICSAW.2017.13(272-277)Online publication date: Apr-2017
https://doi.org/10.1109/ICSAW.2017.13

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