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    Publications

Journal Article

Leveraging Combinatorial Coverage in ML Product Lifecycle

Documentation Topics

Published: June 17, 2024
Citation: Computer (IEEE Computer) vol. 57, no. 7, (July 2024) pp. 16-26

Author(s)

Jaganmohan Chandrasekaran (Virginia Tech), Erin Lanus (Virginia Tech), Tyler Cody (Virginia Tech), Laura Freeman (Virginia Tech), Raghu Kacker (NIST), M S Raunak (NIST), Richard Kuhn (NIST)

Abstract

The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to address key limitations in performing test and evaluation for ML-enabled systems.

The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to address key limitations in performing test... See full abstract

The data-intensive nature of machine learning (ML)-enabled systems introduces unique challenges in test and evaluation. We present an overview of combinatorial coverage, exploring its applications across the ML-enabled system lifecycle and its potential to address key limitations in performing test and evaluation for ML-enabled systems.


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Keywords

Machine Learning; Combinatorial Coverage; Combinatorial Testing; Test generation; Model Maintenance; Regression Testing
Control Families

None selected

Documentation

Publication:
https://doi.org/10.1109/MC.2024.3366142
Preprint (pdf)

Supplemental Material:
None available

Document History:
06/17/24: Journal Article (Final)

Topics

Security and Privacy

testing & validation

Technologies

artificial intelligence, combinatorial testing

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