Optimize Code Review with Qual: A GPT-based Web Platform Integrated with GitHub
Introduction
As a full-stack engineer, I have witnessed the struggles that engineering organizations face during the code review process. Pull request reviews are often lengthy and complex, delaying developers from releasing features and bug fixes in a timely manner. In an age where time-to-market is critical, optimizing code review is essential to maintain a competitive edge.
With my passion for innovation and drive to solve complex engineering challenges, I embarked on a journey to create Qual — a GPT-based web platform designed to enhance code review by providing in-depth insights and automation within the native GitHub workflow.
In this blog post, I will share with you my experience in developing Qual and how it can revolutionize the code review process for software organizations.
The Code Review Challenge
Code review is an integral part of the software development process, helping to identify and fix issues, maintain code quality, and share knowledge among team members. However, the process can be time-consuming and error-prone, with reviewers and developers frequently struggling to understand the full context of code changes.
Moreover, identifying a variety of coding issues — such as logical defects, readability issues, performance concerns, best practices violations, and maintainability problems — can be challenging, even for experienced developers.
Introducing Qual: A GPT-based Solution to Streamline Code Review
Our solution, Qual, leverages the power of AGI to provide faster and higher-quality code review, ultimately speeding up the bottleneck in the software development process. By integrating seamlessly with GitHub, Qual analyzes code changes and delivers actionable insights directly within the developers’ and reviewers’ native workflows. This enables them to quickly identify and address issues, reducing the time spent on code review and enhancing the overall quality of software development.
Key Features of Qual
- Easy Installation and Configuration: the tool aims to simplify installation and integration with GitHub. The configuration process is flexible, allowing users to customize team member access, choose the preferred GPT model, control the GitHub app installation status, selection of specific repositories for the tool to run on.
- Automated Pull Request Analysis: Qual automatically reviews pull request changes, including CI/CD failed workflows, and provides insights on potential fixes or issues.
- Code Selection and Inquiry: With Qual, developers can select specific code sections and ask for explanations, fixes, or issues to be found, streamlining communication between the developer and the reviewer.
- Sentiment Analysis: Qual’s sentiment analysis feature helps maintain a healthy and constructive code review environment by analyzing review comments and alerting users to potentially negative or unproductive communication.
Technical Implementation
To build Qual, I used Firebase infrastructure for the backend, with Firestore as the database and Firebase authentication for secure user management. Cloud Functions written in TypeScript were used to create a serverless backend. This setup allowed for efficient handling of user requests and ensured that the application could scale as needed.
Qual’s frontend was built using React with TypeScript and Mobx for optimal performance. Vite was used as the build tool to ensure fast and efficient development. The frontend features a user-friendly interface with login/signup functionality, an installation wizard, and tool configuration pages. These include member control, open API key settings, GPT model selection, and more.
To seamlessly integrate Qual with the GitHub platform, I used GitHub application webhooks and the Octokit library to communicate with the GitHub API. This integration enables users to manage their repositories and perform various actions without leaving the Qual application.
The Future of Code Review with Qual
As creators in the technology industry, it is essential to constantly evaluate our place in the market and assess the competition. After recent announcements from GitHub regarding their Copilot for Pull Requests feature, we have made the decision to temporarily pause our efforts on Qual.
While we believe that Qual has significant potential and can provide unique value to software development organizations, we recognize that it is difficult to compete with a giant like Microsoft Corporation that has a stronghold on the field.
We decided to be strategic in our efforts and prioritize areas where we can make the biggest impact. At this time, we feel that it is best to shift our focus to other areas of development that may be more fruitful for us.
Conclusion
Although we won’t be continuing to develop Qual, the journey of building it has been an incredible learning experience for us. We’re proud of the progress we made and believe the knowledge and skills we’ve gained will be invaluable in future projects.
While we may not have revolutionized the code review process with AGI, we’re excited to take the lessons we’ve learned and apply them to new challenges ahead.
