How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms

TL;DR

This paper introduces a comprehensive taxonomy for diagnosis computation algorithms to standardize their assessment, facilitate comparison, and aid in selecting suitable methods for specific diagnostic problems.

Contribution

It provides a structured classification framework that helps researchers and practitioners understand, compare, and choose diagnosis algorithms effectively.

Findings

Developed a taxonomy for diagnosis computation methods

Enabled standardized assessment and comparison of algorithms

Facilitated selection of appropriate diagnostic techniques

Abstract

This work proposes a taxonomy for diagnosis computation methods which allows their standardized assessment, classification and comparison. The aim is to (i) give researchers and practitioners an impression of the diverse landscape of available diagnostic techniques, (ii) allow them to easily retrieve the main features as well as pros and cons of the approaches, (iii) enable an easy and clear comparison of the techniques based on their characteristics wrt. a list of important and well-defined properties, and (iv) facilitate the selection of the "right" algorithm to adopt for a particular problem case, e.g., in practical diagnostic settings, for comparison in experimental evaluations, or for reuse, modification, extension, or improvement in the course of research.