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Will it run?-A proof of concept for smoke testing decentralized data analytics experiments.

Sascha Welten1, Sven Weber1,2, Adrian Holt1

  • 1Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany.

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This study introduces DEATHSTAR, a tool for Smoke Testing distributed analytics (DA) to prevent failures in data-driven medicine research. It ensures 96.6% of analyses run successfully, improving reliability for initiatives like the European Health Data Space.

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decentralized applicationsfederated learningmachine learningsimulationsoftware testingweb services

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Area of Science:

  • Health Informatics
  • Data Science
  • Software Engineering

Background:

  • Growing demand for privacy-preserving data analysis in data-driven medicine, exemplified by initiatives like the European Health Data Space (EHDS).
  • Distributed Analytics (DA) offers potential for multi-source data analysis but introduces challenges like identifying single points of failure (SPOFs).
  • Undetected SPOFs can lead to code malfunctions, research delays, and increased costs.

Purpose of the Study:

  • To address the challenge of identifying SPOFs in Distributed Analytics (DA) tasks before execution.
  • To introduce and evaluate a novel approach for ensuring the operability of DA analysis code through Smoke Testing.
  • To develop an interactive environment for researchers to perform Smoke Tests on DA experiments.

Main Methods:

  • Reviewed existing DA platforms to extract six specific Smoke Testing criteria for DA applications.
  • Developed the Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR) based on these criteria.
  • Conducted a user study with 29 participants and applied DEATHSTAR to three real-world use cases.

Main Results:

  • The DEATHSTAR environment was assessed for its effectiveness in performing Smoke Tests on DA experiments.
  • A user study demonstrated that 96.6% of analyses created and tested using the DEATHSTAR approach terminated successfully without errors.
  • The approach effectively identifies potential malfunctions early in the development process.

Conclusions:

  • Incorporating Smoke Testing as a fundamental method significantly enhances the reliability of data-driven research using DA.
  • DEATHSTAR provides a flexible and adaptable solution for robust and efficient development of DA experiments.
  • The developed approach contributes to smoother, more reliable, and cost-effective data-intensive research.