一句话：提出用静态分析自动推断代码约束，显式化数据分析脚本中的隐式假设以提升可复现性。
在 R 或 Python 等高层语言中，数据分析常以脚本或 notebook 形式传播，却因作者未记录的隐式假设而难以复现；这些假设包括包版本、数据表形状以及手工设置步骤。本文提出一个统一的、由示例驱动的视角来刻画数据分析中的隐式假设，并给出探索性概念验证实现。方法上，作者主张用静态分析技术识别此类假设，并将其显式表达为针对数据分析问题的代码约束。这些约束可用于自动将脚本转换为可执行、可复现的工件，在运行时检查假设，并充当支持代码复用与理解的文档。评估以原型实现展示该思路的可行性。
High-level languages such as R or Python are used frequently to analyze and visualize data in the form of scripts or notebooks. However, these artifacts suffer from reproducibility issues due to what we frame as implicit assumptions made by the authors. Such assumptions range from package versions and shapes of involved data tables, to manual and often undocumented setup steps. Within this work, we provide a unified, example-driven perspective on implicit assumptions in data analysis backed by an explorative proof-of-concept implementation. With this perspective, we propose the use of static analysis techniques to identify such assumptions and to make them explicit in the form of code constraints, focusing on the inclusion of data-analysis-specific issues. Such constraints can then be used to automatically transform these scripts into executable and reproducible artifacts, to check these assumptions at runtime, and to serve as documentation to support code reuse and comprehension.
@inproceedings{paperbot12039,
title = {Towards Automatically Inferring Constraints to Identify Implicit Assumptions in Data Analysis},
author = {Florian Sihler and Lars Pfrenger and Oliver Gerstl and Matthias Tichy},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3786582.3786806}
}