Who should use the Automated Debugging workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for automated debugging with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
The fix is live in production with no observed regressions, and monitoring confirms the bug is resolved.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
The fix is live in production with no observed regressions, and monitoring confirms the bug is resolved.
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 Devin to a clear, reproducible bug report with the exact location and context of the failure. Then, you pass the output to Digma SRE AI Platform to a prioritized list of root cause hypotheses with supporting evidence from static analysis and version history. Then, you pass the output to Devin to a validated patch that fixes the bug without introducing new failures, ready for code review. Then, you pass the output to Qodo CodeAI (formerly CodiumAI) to a polished, human-reviewed fix with dedicated test coverage, ready for merge. Finally, Datadog is used to the fix is live in production with no observed regressions, and monitoring confirms the bug is resolved.
Reproduce and Isolate the Bug
A clear, reproducible bug report with the exact location and context of the failure.
Generate Hypotheses and Root Cause Analysis
A prioritized list of root cause hypotheses with supporting evidence from static analysis and version history.
Automate Patch Generation and Validation
A validated patch that fixes the bug without introducing new failures, ready for code review.
Review and Refine the Fix
A polished, human-reviewed fix with dedicated test coverage, ready for merge.
Deploy and Monitor the Fix
The fix is live in production with no observed regressions, and monitoring confirms the bug is resolved.
First, ensure the bug is consistently reproducible in a controlled environment. Run the failing test or scenario, capture the exact error message, stack trace, and relevant logs. Use a debugger or logging framework to narrow down the module or function where the failure occurs.
Why Devin: Devin is designed for end-to-end bug fixing and debugging, which directly covers reproducing and isolating bugs with integrated debugging capabilities.
Analyze the isolated failure to hypothesize potential root causes. Use static analysis tools to check for common patterns (null pointers, off-by-one errors, race conditions). Cross-reference with recent code changes using git bisect or diff tools to identify the commit that introduced the bug.
Why Digma SRE AI Platform: Digma SRE AI Platform specializes in root cause analysis and code issue identification, directly matching the needs of generating hypotheses and RCA.
For each hypothesis, generate a candidate fix automatically using a code repair tool or a rule-based transformation. Apply the patch to the codebase, then run the existing test suite to verify the fix doesn't break other functionality. If tests pass, run a broader integration test.
Why Devin: Devin handles end-to-end bug fixing and code refactoring, which includes automated patch generation and validation.
Examine the generated patch for code quality, readability, and adherence to project standards. If the patch is suboptimal (e.g., too narrow or introduces technical debt), manually refine it. Optionally, run a diff coverage tool to ensure the fix is covered by new or existing tests.
Why Qodo CodeAI (formerly CodiumAI): Qodo CodeAI (formerly CodiumAI) offers AI-powered pull request reviews and code refactoring, directly supporting code review and refinement.
Merge the fix into the main branch and deploy to a staging environment first. Monitor application logs, error rates, and key metrics (e.g., latency, error count) for at least one full cycle. If no anomalies appear, promote to production with a gradual rollout.
Why Datadog: Datadog provides infrastructure monitoring, APM, and log aggregation, directly covering deployment monitoring needs.
§ Before you start
Teams or solo builders working on work 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.
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