Who should use the Code Analysis workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for code analysis with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A finalized production code is ready for publishing, handoff, or integration.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A finalized production code is ready for publishing, handoff, or integration.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use OpenText Fortify to inputs, context, and settings are ready so the workflow can move into execution without blockers. Then, you pass the output to Graphite to supporting assets from ai-powered code analysis are prepared and connected to the main workflow. Then, you pass the output to MeetLeo (Brave Leo AI) to supporting assets from refactor code are prepared and connected to the main workflow. Then, you pass the output to Swimm to a first-pass production code is generated and ready for refinement in the next steps. Then, you pass the output to GitHub Copilot to the production code is improved, validated, and prepared for final delivery. Then, you pass the output to Mistral AI Models to the production code is improved, validated, and prepared for final delivery. Finally, GitHub Copilot is used to a finalized production code is ready for publishing, handoff, or integration.
Static Code Analysis
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
AI-Powered Code Analysis
Supporting assets from ai-powered code analysis are prepared and connected to the main workflow.
Refactor code
Supporting assets from refactor code are prepared and connected to the main workflow.
Code Analysis
A first-pass production code is generated and ready for refinement in the next steps.
Generate code documentation
The production code is improved, validated, and prepared for final delivery.
Generate code snippets
The production code is improved, validated, and prepared for final delivery.
Debug code
A finalized production code is ready for publishing, handoff, or integration.
Prepare inputs and settings through Static Code Analysis before running code analysis.
Static Code Analysis sets up the foundation for code analysis; clean inputs here reduce downstream rework.
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Use AI-Powered Code Analysis to build supporting assets that improve code analysis quality.
AI-Powered Code Analysis strengthens code analysis by feeding better supporting material into the pipeline.
Supporting assets from ai-powered code analysis are prepared and connected to the main workflow.
Use Refactor code to build supporting assets that improve code analysis quality.
Refactor code strengthens code analysis by feeding better supporting material into the pipeline.
Supporting assets from refactor code are prepared and connected to the main workflow.
Execute code analysis with Code Analysis to produce the primary production code.
This is the core step where code analysis actually happens, so it determines baseline quality for everything after it.
A first-pass production code is generated and ready for refinement in the next steps.
Refine and validate code analysis output using Generate code documentation before final delivery.
Generate code documentation adds quality control so issues are caught before the workflow is finalized.
The production code is improved, validated, and prepared for final delivery.
Refine and validate code analysis output using Generate code snippets before final delivery.
Generate code snippets adds quality control so issues are caught before the workflow is finalized.
The production code is improved, validated, and prepared for final delivery.
Package and ship the output through Debug code so code analysis reaches end users.
Debug code is what turns intermediate output into a usable, publishable result for real users.
A finalized production code is ready for publishing, handoff, or integration.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
A streamlined workflow to prepare data, train a neural network model, and evaluate its performance using AI tools.
Streamlined workflow to automatically refactor existing code, debug errors, and finalize the refactored code for deployment.
End-to-end workflow to orchestrate data pipelines: start by performing predictive analytics to inform the pipeline, then orchestrate the data flow, and finally monitor model performance for ongoing reliability.