为医疗等数据稀缺系统设计可保留关键属性的合成数据
Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
- 以关键属性为导向生成合成数据，而非盲目模拟真实数据
- 通过与肿瘤医生协作，识别出需保留的临床有效性属性
- 适合医疗、安全关键领域中数据受限的软件系统开发
现代软件系统越来越依赖数据进行分析、预测、测试和决策。然而，在医学、高安全性系统和受监管行业等重要领域，往往缺乏充足、可共享或具代表性的数据。合成数据生成常被视为解决方案，但我们在乳腺癌术中放疗（IORT）系统开发中的经验表明，合成数据仅转移了核心工程难题。关键挑战在于确定合成数据必须保留哪些属性、如何从利益相关方获取这些属性、在隐私约束下如何验证，以及如何随时间演化。我们称之为属性驱动的合成数据工程。基于与肿瘤医生的合作及对敏感IORT数据集的初步实验，我们识别出需求定义、验证、隐私保护和流水线演进方面的挑战。我们认为，自动化软件工程研究应发展方法与工具，用于在数据稀缺系统中获取、形式化、检验和演化合成数据的有效性属性。
原文摘要 · Abstract (English)
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.
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