# Towards Testing of Deep Learning Systems with Training Set Reduction

arXiv: https://arxiv.org/abs/1901.04169 · HTML: https://mlsanity.com/paper/1901.04169/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques](https://mlsanity.com/paper/2202.12139/) (2022)

## Abstract (arXiv)

Testing the implementation of deep learning systems and their training routines is crucial to maintain a reliable code base. Modern software development employs processes, such as Continuous Integration, in which changes to the software are frequently integrated and tested. However, testing the training routines requires running them and fully training a deep learning model can be resource-intensive, when using the full data set. Using only a subset of the training data can improve test run time, but can also reduce its effectiveness. We evaluate different ways for training set reduction and their ability to mimic the characteristics of model training with the original full data set. Our results underline the usefulness of training set reduction, especially in resource-constrained environments.