An interactive web tool that visualizes a weighted knowledge graph containing 2,376 version knowledge extracted from Stack Overflow discussions.
Version incompatibility issues are prevalent when reusing or reproducing deep learning (DL) models and applications. While official documents such as PyPI are neither comprehensive nor up-to-date, Stack Overflow (SO) discussions possess a wealth of version knowledge that has not been explored by previous approaches. To bridge this gap, we present WebDecide, a web-based visualization of a weighted knowledge graph that contains 2,376 version knowledge extracted from SO discussions. As an interactive tool, WebDecide allows the users to easily check whether two libraries are compatible or not, search for a third-party DL component with a specific version, explore a DL component without any version constraint, and shows relevant SO posts as references.
- Node.js: Ensure that you have Node.js installed on your system. You can download the latest version from the official Node.js website (https://nodejs.org) and follow the installation instructions for your operating system.
- Clone or download this github repository:
git clone https://github.com/LexieZhou/Decide.git
- Install dependencies
- Navigate to the backend directory:
cd server - Install the requirements:
npm install - Navigate to the frontend directory:
cd ../client - Install the requirements:
npm install
- Navigate to the backend directory:
- Start the Project
- Navigate to the backend directory:
cd server - Start the server:
node server.js - Navigate to the frontend directory:
cd ../client - Start the project in browser:
npm start
- Navigate to the backend directory:
Remark1: If encounter any dependency error, try npm install --force instead.
Remark2: If you want to enable function calling ability of GPT-4 in the search feature, you could navigate to the client/data directory, find config.json file, and input your API key.
Users can zoom in, zoom out, and drag to explore the details. Each node inside this knowledge graph is a versioned deep learning stack component. When hovering over the node, users can see the detailed information about this node in the right corner. The links between nodes represent their (in)compatibility relationships. Solid links indicate compatibility, while dashed links indicate an incompatibility relationship between two components.
- Click certain component
- The information panel will present detailed information about the library sourced from Libraries.io.
- Click certain link
- The information panel will show details about two components, their detected (in)compatibility relationship, an evidence score, and supporting posts. Additionally, you can explore posts supporting both the compatible and incompatible relationships. Clicking on the vote link takes you directly to the original posts on the Stack Overflow platform.
Decide supports three kinds of searches, which is integrated with the function calling ability of GPT-4 and also regex pattern matching.
- Quickly check the compatibility between two deep learning components by entering a simple query.
- e.g. Does Python 3.6.8 work with Ubuntu 16.04.6?
- Search for compatibility knowledge about versioned deep learning components.
- e.g. Ubuntu 14.04
- Explore compatibility knowledge for deep learning stack components with no version specified.
- e.g. Tensorflow
Decide shows statistical information about the top entities in each deep learning layer in the left panel. Hovering over a bar allows you to filter the knowledge graph to display the corresponding deep learning stack component and its compatibility relationships in the Compatibility Visualizer.
Evaluation in our technical paper shows that Decide accurately identify 65% of known version issues in 10 popular DL projects with a high precision (92%), while two state-of-the-art approaches PyEGo and Watchman can only detect 29% and 6% of these issues with 33% and 17% precision respectively. Additionally, after independent verification manually, 287 relations from 343 samples were confirmed as correct, resulting in an overall accuracy of 83.7% for knowledge graph quality.