A Comprehensive Evaluation of Taxonomy Quality Attributes

Core Concepts

The author aims to identify and define taxonomy quality attributes for practical measurements, helping researchers and practitioners choose the most suitable taxonomy. The approach involves reviewing publications, proposing quality attributes, and providing measurements.

Abstract

The content discusses the importance of evaluating taxonomy quality attributes in software engineering. It highlights the need for practical measurements to compare and select appropriate taxonomies based on seven defined quality attributes. The evaluation process involves reviewing literature, proposing measurements, and demonstrating their usefulness. Received: Added at production Revised: Added at production Accepted: Added at production DOI: xxx/xxxx SPECIAL ISSUE PAPER A compendium and evaluation of taxonomy quality attributes Michael Unterkalmsteiner* | Waleed Abdeen 1Software Engineering Research and Education Lab Sweden, Blekinge Institute of Technology, Sweden Correspondence *Michael Unterkalmsteiner, Valahallavägen 1, Karlskrona, 37141 Sweden. Email: [e-mail] Abstract Introduction: Taxonomies capture knowledge about a particular domain in a succinct manner and establish a common understanding among peers. Researchers use taxonomies to convey information about a particular knowledge area or to support automation tasks, and practitioners use them to enable communication beyond organizational boundaries. Aims: Despite this important role of taxonomies in software engineering, their quality is seldom evaluated. Our aim is to identify and define taxonomy quality attributes that provide practical measurements, helping researchers and practitioners to compare taxonomies and choose the one most adequate for the task at hand. Methods: We reviewed 324 publications from software engineering and information systems research and synthesized when provided the definitions of quality attributes and measurements. We evaluated the usefulness of the measurements on six taxonomies from three domains. Results: We propose the definition of seven quality attributes...

Stats

Count of unclassified objects Count of taxonomy constructs

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A compendium and evaluation of taxonomy quality attributes

by Michael Unte... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00111.pdf

Deeper Inquiries

How can external measures be used to evaluate internal taxonomy attributes?

External measures can be used to indirectly assess internal taxonomy attributes by observing the behavior of the taxonomy in a particular environment. For example, when evaluating the comprehensiveness of a taxonomy (an internal attribute), an external measure like counting unclassified objects can provide insights into how well the taxonomy is able to classify all known objects. Similarly, for assessing robustness (another internal attribute), external measures such as semantic proximity and distance analysis can help determine the degree to which the taxonomy's categories and characteristics represent distinct concepts.

How does comprehensiveness impact taxonomy usability?

Comprehensiveness plays a crucial role in determining the usability of a taxonomy. A comprehensive taxonomy that classifies all known objects within its domain effectively enables users to locate and categorize new objects accurately. This leads to improved user experience, as users can easily navigate through the taxonomy and find relevant information without ambiguity or confusion. In contrast, a non-comprehensive taxonomy may result in misclassifications, leading to decreased usability and potentially hindering decision-making processes based on the taxonomic structure.

How can robustness be balanced with conciseness in developing a taxonomy?

Balancing robustness with conciseness in developing a taxonomy involves ensuring that the classification system is both thorough in differentiating between objects (robust) while also being efficient and not overly complex (concise). One approach is to prioritize essential dimensions, categories, and characteristics that are necessary for accurate classification while avoiding unnecessary complexity that could lead to overlapping or redundant classifications. By focusing on clarity and relevance in defining taxonomic elements, developers can strike a balance between robustness – ensuring accurate differentiation – and conciseness – maintaining simplicity for ease of use.