Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
arXiv:2607.06133v1 Announce Type: cross Abstract: Modern software systems increasingly depend on data for analysis, prediction, testing, and decision-making. Yet many important domains, including medicine, safety-critical systems, and regulated industries, lack abundant, shareable, or representative data. Synthetic data generation is often proposed as a remedy, but our experience engineering software for intraoperative radiotherapy (IORT) in breast cancer treatment suggests that synthetic data shifts rather than solves the central engineering problem. The key challenge becomes deciding which p
The increasing reliance of modern software systems on data, coupled with data scarcity in critical domains, makes the generation of high-quality synthetic data a pressing technical problem to solve.
This highlights the fundamental challenge of data availability for AI and advanced software in sensitive sectors, pushing the limits of current synthetic data generation methods.
The focus regarding synthetic data shifts from mere generation to property-driven engineering, emphasizing the need for data that accurately reflects real-world characteristics for reliable system development.
- · AI data scientists
- · Healthcare technology developers
- · Critical infrastructure software providers
- · Organizations reliant on simple synthetic data generation
- · Sectors experiencing severe data scarcity
Improved development and testing of data-hungry software systems in sensitive domains like medicine due to better synthetic data.
Accelerated innovation in areas constrained by data access, leading to new AI applications and products in regulated industries.
Enhanced trust in AI systems deployed in critical applications due to more rigorously tested and validated models trained on property-driven synthetic data.
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Read at arXiv cs.AI