# Towards a Common Testing Terminology for Software Engineering and Data Science Experts

arXiv: https://arxiv.org/abs/2108.13837 · HTML: https://mlsanity.com/paper/2108.13837/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Quality Assurance for AI-based Systems: Overview and Challenges](https://mlsanity.com/paper/2102.05351/) (2021)

## Built on by (most cited first, 5 of 5)

- [Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components](https://mlsanity.com/paper/2202.05313/) (2022)
- [Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning](https://mlsanity.com/paper/2305.14872/) (2023)
- [Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems](https://mlsanity.com/paper/2405.13191/) (2024)
- [Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning](https://mlsanity.com/paper/2312.04917/) (2023)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)

## Abstract (arXiv)

Analytical quality assurance, especially testing, is an integral part of software-intensive system development. With the increased usage of Artificial Intelligence (AI) and Machine Learning (ML) as part of such systems, this becomes more difficult as well-understood software testing approaches cannot be applied directly to the AI-enabled parts of the system. The required adaptation of classical testing approaches and the development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. We see the different terminologies used in the two communities as a major obstacle on this way. As we consider a mutual understanding of the testing terminology a key, this paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in the relevance and naming of the mapped concepts.