# The challenge of generating and evolving real-life like synthetic test data without accessing real-world raw data -- a Systematic Review

arXiv: https://arxiv.org/abs/2602.06609 · HTML: https://mlsanity.com/paper/2602.06609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Natural Language Generation for Electronic Health Records](https://mlsanity.com/paper/1806.01353/) (2018)
- [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023)
- [CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry](https://mlsanity.com/paper/2211.14054/) (2022)
- [Modality-aware Transformer for Financial Time series Forecasting](https://mlsanity.com/paper/2310.01232/) (2023)
- [Practical Constraint Solving for Generating System Test Data](https://mlsanity.com/paper/1902.00397/) (2019)
- [Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets](https://mlsanity.com/paper/2410.17651/) (2024)

## Abstract (arXiv)

Background: High-level system testing of applications that use data from e-Government services as input requires test data that is real-life-like but where the privacy of personal information is guaranteed. Applications with such strong requirement include information exchange between countries, medicine, banking, etc. This review aims to synthesize the current state-of-the-practice in this domain. Objectives: The objective of this Systematic Review is to identify existing approaches for creating and evolving synthetic test data without using real-life raw data. Methods: We followed well-known methodologies for conducting systematic literature reviews, including the ones from Kitchenham as well as guidelines for analysing the limitations of our review and its threats to validity. Results: A variety of methods and tools exist for creating privacy-preserving test data. Our search found 1,013 publications in IEEE Xplore, ACM Digital Library, and SCOPUS. We extracted data from 75 of those publications and identified 37 approaches that answer our research question partly. A common prerequisite for using these methods and tools is direct access to real-life data for data anonymization or synthetic test data generation. Nine existing synthetic test data generation approaches were identified that were closest to answering our research question. Nevertheless, further work would be needed to add the ability to evolve synthetic test data to the existing approaches. Conclusions: None of the publications really covered our requirements completely, only partially. Synthetic test data evolution is a field that has not received much attention from researchers but needs to be explored in Digital Government Solutions, especially since new legal regulations are being placed in force in many countries.