Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
For researchers and practitioners in data-scarce domains (e.g., medicine, safety-critical systems), this paper highlights a fundamental but overlooked challenge in synthetic data engineering, though it remains at the problem-identification stage without concrete solutions.
The paper argues that synthetic data generation shifts rather than solves the data scarcity problem, introducing the concept of property-driven synthetic data engineering. Based on experience in the breast cancer domain, it identifies challenges in requirements, validation, privacy, and pipeline evolution, calling for automated software engineering methods to handle validity properties.
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 properties synthetic data must preserve, how these properties should be elicited from stakeholders, how they can be validated under privacy constraints, and how they evolve. We call this problem property-driven synthetic data engineering. Drawing on a collaboration with oncologists and preliminary experiments with a sensitive IORT dataset, we identify challenges in requirements, validation, privacy, and pipeline evolution. We argue that automated software engineering research should develop methods and tools for eliciting, formalizing, checking, and evolving validity properties for synthetic data in data-scarce software systems.