Phase 2: Missing Story Detection

Phase 2: Missing Story Detection identifies functionality that exists in the codebase but is not documented as user stories. WalnutAI also validates existing stories to assess coverage and provides AI-driven suggestions for improving and updating them. In this phase, the code acts as the baseline, and WalnutAI detects undocumented or incomplete requirement coverage.

• Before starting the analysis:

  • The project must be connected to one or more repositories.

  • Ensure the repositories were properly integrated during project creation.

  • The codebase from the selected repositories will be used as the reference for comparison against existing user stories.

• To start the analysis:

  • Click Start Analysis

  

  • Select one or more connected repositories.

  

  • Click Start Analysing to begin evaluation.

  
• During analysis:

  • WalnutAI scans the selected repository and branch.

  • It compares implemented code with documented user stories.

  • It identifies functionality implemented in code but not documented as a story.

  • It detects stories that require corrections or acceptance criteria improvements.

  • It enhances acceptance criteria to fully reflect implemented behaviour.

  • It generates new user story suggestions for uncovered features.

  
• After the analysis completes, the dashboard displays:

  • Stories Analysed – Total number of stories reviewed.

  • Corrections Needed – Stories requiring refinement.

  • Missing Stories – Undocumented features detected from code.

  • High Priority – Critical missing or impacted stories.

• In the Analysis Results section:

  • Separate tabs for Corrected Stories and Missing Stories with respective counts.

• For Corrected Stories:

  • Click on Option to Select All or select individual stories and directly Apply Correction using the Apply Correction action.

  

  • Clear selection option to reset selections.

  

  • Click on Story for Side-by-side comparison of Original and Corrected versions.

  

  • Updated Title, Description, and Acceptance Criteria suggested by Walnut AI.

  • Highlighted improvements in acceptance criteria coverage.

  • Confidence percentage indicating alignment strength.

  • AI Reasoning explaining why corrections are recommended.

  
• For Missing Stories:

  • Click on Option to Select All or select individual stories and directly create user stories using the Create Story action.

  

  • Clear selection option to reset selections.

  

  • Suggested story title derived from code behaviour.

  • Priority level (e.g., High).

  • The Confidence Score indicates how strongly the detected functionality aligns with the suggested or corrected user story based on Walnut AI’s semantic and structural analysis.

  • Functionality Description generated from implementation.

  • Suggested Acceptance Criteria based on detected logic.

  • Related Code Files showing impacted source files.

  
• Click Run New Analysis to scan the updated codebase and user stories again, ensuring newly implemented features and recent changes are reflected in the analysis results

Phase 2 ensures implemented features are fully documented, improves requirement and acceptance criteria quality, maintains code-to-requirement traceability, and keeps documentation aligned with actual system behaviour while preventing undocumented development.