Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain

2026-07-07

AI Summary

This work addresses the limited effectiveness of synthetic data in data-scarce clinical scenarios—such as intraoperative radiotherapy for breast cancer—where success hinges on the identification and management of critical attributes. The authors propose an attribute-driven synthetic data engineering approach that reframes validity as an engineerable task of attribute lifecycle management. Collaborating closely with oncologists, they systematically develop a synthetic data framework tailored to high-sensitivity medical domains, spanning requirement definition, formal modeling, privacy-preserving validation, and process evolution. Beyond uncovering core challenges in synthetic data engineering for data-scarce software systems, this study advances automated software engineering by introducing mechanisms for collaborative attribute specification and continuous evolution.

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 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.

Problem

• synthetic data
• data scarcity
• property-driven
• software engineering
• privacy constraints

Innovation

• property-driven
• synthetic data engineering
• data-scarce systems
• validity properties
• privacy-aware validation