一句话：提出生物感知软件工程（BASE）愿景：让隐形 AI 智能体观察探索性分析，借助领域知识图谱自动合成容器、回归测试与文档等基础设施。
生物信息学处于一个脆弱的交叉点：生物学问题前沿，但支撑研究的软件基础设施往往临时、易碎且短命。本文通过对生物信息学研究者的定性研究反思实践现状，发现系统性失败：计算机背景的研究者难以跨越生物学「知识鸿沟」，而生物学背景的研究者则深陷「安装地狱」与碎片化文档；同时，尽管 AI 工具被日益采用，其使用仍停留在生成孤立代码片段的表层，未能解决结构性的工程债。作者据此论证，当前要求科学家兼任系统工程专家的做法不可扩展，并提出「生物感知软件工程」（BASE）愿景：一种 AI 驱动范式，由「隐形」智能体观察探索性分析过程，并利用领域特定知识图谱自动合成必要的软件基础设施，包括容器、回归测试与文档。该愿景指向更可复现、更可维护的生物信息学工作流。
Bioinformatics stands at a precarious intersection: while biological questions are cutting-edge, the software infrastructure supporting them is often fragile, "ad hoc", and ephemeral. In this paper, we reflect on the state of the practice through a qualitative study of bioinformatics researchers. Our findings reveal a systemic failure: researchers with Computer Science backgrounds struggle to bridge the biological "knowledge gap", while those with Biology backgrounds are bogged down by "installation hell" and fragmented documentation. Furthermore, we observe that while AI tools are increasingly adopted, their usage remains superficial—restricted to generating isolated code snippets rather than addressing structural engineering debt. We argue that the current solution—expecting scientists to act as expert systems engineers—is unscalable. We propose a vision of Bio-Aware Software Engineering (BASE): an AI-driven paradigm where "invisible" agents observe exploratory analysis and automatically synthesize the necessary software infrastructure (containers, regression tests, and documentation) by leveraging domain-specific knowledge graphs. This vision points toward more reproducible and maintainable bioinformatics workflows.
@inproceedings{ZengWGKLY26,
title = {Bio-Aware Software Engineering for Reproducible Research},
author = {Yiming Zeng and Dingbang Wang and Ryan Green and Sandeep Kaur Kuttal and Jinze Liu and Tingting Yu},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805577}
}