Inspiration
Debugging is one of the most time-consuming parts of software development. Developers often spend hours understanding bug reports, tracing possible causes, testing fixes, and identifying regression risks. We wanted to simplify this process by building FixLoop, an AI-powered debugging assistant that helps developers move from a bug report to a proposed solution faster.
What it does
FixLoop takes a bug description and the relevant source code, then generates a structured debugging report containing:
- Root-cause analysis: Identifies likely reasons for the reported bug.
- Suggested code fix: Provides a proposed patch for the developer to review.
- Regression-test recommendations: Suggests tests to help prevent the same issue from returning.
- Risk analysis: Highlights potential side effects and considerations.
- Code comparison: Displays differences between buggy and corrected code in the guided demo.
The project includes an offline demonstration mode, allowing users to explore the workflow without an API key or paid cloud access. Live AI analysis can be configured with an authorized Nebius Token Factory API key and a supported model.
How we built it
We built FixLoop as a Python-based web application using Streamlit for the interface. The application accepts bug reports and source-code snippets, processes the input through a debugging-analysis workflow, and presents the results in a developer-friendly format.
The prototype includes a guided calculator-bug demonstration, a code-difference viewer, regression-test demonstrations, and downloadable Markdown reports. We also added automated tests and a GitHub Actions workflow to support basic code validation.
The architecture separates the user interface, analysis workflow, and optional AI-provider configuration so that the project can be demonstrated offline and extended with live model inference when access is available.
Challenges we faced
One of our main challenges was designing a useful debugging workflow without making the system depend entirely on paid API access. We addressed this by adding an offline demo mode.
Another challenge was ensuring that suggested fixes remain reviewable rather than being applied blindly. FixLoop displays proposed patches for developers to inspect instead of automatically modifying their source code. We also added a controlled test demonstration to show how a bug can be reproduced and how tests can help validate a correction.
What we learned
Building FixLoop helped us understand how to connect AI-assisted reasoning with practical software-development workflows. We learned the importance of structured model outputs, reproducible bug demonstrations, regression testing, safe handling of source code, and clear separation between a suggested fix and a verified fix.
What's next
Our next steps include testing live inference with an authorized AI model, supporting repository-level analysis, validating generated patches against automated test suites, and adding safer workflows for reviewing and applying changes through version control.
Our goal: Help developers spend less time investigating bugs and more time building reliable software.
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