# Automatic Identification and Extraction of Assumptions on GitHub

arXiv: https://arxiv.org/abs/2303.06822 · HTML: https://mlsanity.com/paper/2303.06822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks](https://mlsanity.com/paper/2401.03653/) (2024)

## Abstract (arXiv)

In software development, due to the lack of knowledge or information, time pressure, complex context, and many other factors, various uncertainties emerge during the development process, leading to assumptions scattered in projects. Being unaware of certain assumptions can result in critical problems (e.g., system vulnerability and failures). The prerequisite of analyzing and understanding assumptions in software development is to identify and extract those assumptions with acceptable effort. In this paper, we proposed a tool (i.e., Assumption Miner) to automatically identify and extract assumptions on GitHub projects. To evaluate the applicability of Assumption Miner, we first presented an example of using the tool to mine assumptions from one large and popular deep learning framework project: the TensorFlow project on GitHub. We then conducted an evaluation of the tool. The results show that Assumption Miner can effectively identify and extract assumptions from the repositories on GitHub.