Bob DebugFlow

### Problem Software debugging is often a manual and fragmented process. When a developer encounters multiple bugs, they typically need to inspect the codebase, understand the error, locate the root cause, implement a fix, run tests, review the changes, and document the result. When these steps are performed manually, debugging can become time-consuming and inconsistent, especially when several independent issues exist across different parts of a project. ### Solution Bob DebugFlow is an AI-assisted debugging workflow built around IBM Bob 2.0. Instead of treating debugging as a simple “find and fix the error” task, the project organizes the complete debugging lifecycle into a structured workflow. The workflow starts with issue and codebase analysis, followed by root-cause investigation. Bob can investigate different parts of the project in parallel using subagents. Important stages include developer approval gates so that the developer remains involved before the workflow proceeds to major actions. After the causes are confirmed, Bob applies the required fixes, runs the test suite, verifies the results, performs a dedicated code review, and generates structured debugging reports and productivity metrics. For the demonstration, I created a small FastAPI application containing six intentionally seeded bugs covering API behavior, validation, filtering, error handling, calculation, and database resource management. Before the debugging workflow, the project had 7 failing tests out of 17. After the DebugFlow process, all 17 tests passed, with 86% test coverage. The demonstration also recorded four approved human checkpoints. ### Target Users The primary users are software developers, students, engineering teams, and anyone working with Python applications who wants a more structured way to investigate and verify software issues. ### User Interaction A developer starts the DebugFlow workflow in IBM Bob and provides the project context and debugging objective. Bob analyzes the project, investigates the issues, uses parallel subagents where appropriate, presents findings at approval gates, applies fixes, runs tests, performs code review, and generates reports. A lightweight web dashboard provides a visual overview of the workflow, findings, test results, code review results, and productivity metrics. ### Creativity and Uniqueness The key idea is not simply using AI to generate code. Bob DebugFlow treats AI as a workflow orchestrator. It combines structured debugging phases, parallel investigation, human-in-the-loop approval, automated verification, code review, reporting, and measurable productivity tracking in one process. For this demonstration, the estimated manual debugging effort was 45 minutes compared with a 12-minute DebugFlow session, giving an estimated 33 minutes saved. These figures represent the controlled demonstration rather than a scientific benchmark. The result is a reproducible debugging workflow that helps developers move from issue identification to verified resolution and documentation in a single structured process.