Property-Based Testing for Parameter Learning of Probabilistic Graphical Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Code quality is a requirement for successful and sustainable software development. The emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. The diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. The necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction
Subtitle of host publication CD-MAKE 2020
EditorsAndreas Holzinger, Peter Kieseberg, A. Min Tjoa
PublisherSpringer, Cham
Pages499-515
Number of pages17
ISBN (Print)978-3-030-57320-1
DOIs
Publication statusPublished - 1 Jan 2020
Event2020 Cross Domain Conference for Machine Learning and Knowledge Extraction - Virtuell, Ireland
Duration: 25 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science
Volume12279

Conference

Conference2020 Cross Domain Conference for Machine Learning and Knowledge Extraction
Abbreviated titleCD-MAKE 2020
CountryIreland
CityVirtuell
Period25/08/2028/08/20

Keywords

  • Machine learning
  • Probabilistic graphical models
  • Property-based testing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fields of Expertise

  • Information, Communication & Computing

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  • Cite this

    Saranti, A., Taraghi, B., Ebner, M., & Holzinger, A. (2020). Property-Based Testing for Parameter Learning of Probabilistic Graphical Models. In A. Holzinger, P. Kieseberg, & A. Min Tjoa (Eds.), Machine Learning and Knowledge Extraction: CD-MAKE 2020 (pp. 499-515). (Lecture Notes in Computer Science; Vol. 12279). Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_28