Technical Debt Detection & Remediation Assistant

Problem: As codebases grow, developers can accumulate technical debt such as hardcoded secrets, duplicated code, long functions, missing error handling, and dead code. Finding, prioritizing, and addressing these issues manually can increase maintenance effort and rework. Solution: Technical Debt Detection & Remediation Assistant is a local developer tool that provides a complete workflow: Scan → Detect → Prioritize → Remediate → Validate. It analyzes a built-in synthetic Python codebase, detects five categories of technical debt, assigns severity levels, and presents the findings through a lightweight Flask web interface. The system then creates safe text-level remediation patches in a separate output/patched directory, leaving the original codebase unchanged. The patched code is rescanned to validate the changes and compare the results before and after remediation. In the demonstrated sample, the system identifies 10 findings, including 1 critical, 3 high, 1 medium, and 5 low-severity findings. After remediation, 8 findings are resolved and 2 remain, with the remaining limitations clearly reported. Target users: Developers and students working with Python codebases who want a simple way to understand and address common technical-debt issues. Creative approach: Rather than only reporting problems, the project connects detection with prioritization, remediation, and validation in one workflow. It also uses a safe separate patched copy so the original sample codebase is preserved. The prototype runs locally with no external APIs, cloud services, credentials, or personal data.