DeepAI AI Chat
Log In Sign Up

Automatic Identification and Extraction of Assumptions on GitHub

by   Chen Yang, et al.
Wuhan University

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.


page 1

page 2

page 3

page 4


Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study

Deep learning (DL) frameworks have been extensively designed, implemente...

We Don't Need Another Hero? The Impact of "Heroes" on Software Development

A software project has "Hero Developers" when 80 delivered by 20 heroes ...

Dynamic Scheduling and Workforce Assignment in Open Source Software Development

A novel modeling framework is proposed for dynamic scheduling of project...

Bot Detection in GitHub Repositories

Contemporary social coding platforms like GitHub promote collaborative d...

Simulating the Software Development Lifecycle: The Waterfall Model

(1) Background: This study employs a simulation-based approach, adapting...

A Tool to Extract Structured Data from GitHub

GitHub repositories consist of various detailed information about the pr...