| name | calibrate-sizing |
| description | Use to improve story sizing accuracy by analyzing historical estimation data. Identifies patterns, updates size definitions, and refines PROBE proxy selections. |
| version | 1.0.0 |
Calibrate Sizing Task
Purpose
Continuously improve story sizing accuracy using PSP measurement data:
- Analyze actual vs estimated times
- Update size category definitions
- Refine PROBE proxy selections
- Identify estimation patterns
- Improve future sizing precision
Reference Materials
For detailed YAML examples of all structures below, see ./reference/probe-methodology.md.
SEQUENTIAL Task Execution
1. Gather Historical Data
Load completed stories from last 10-20 completions. Collect size distribution data including count, estimated average, actual average, and variance for each size category (VS, S, M, L, VL). Flag Very Large stories for potential breakdown.
2. Identify Patterns
Analyze Systematic Biases:
Identify overestimation patterns (e.g., Backend CRUD), underestimation patterns (e.g., third-party integrations), and accurate patterns. Document technology factors (new vs familiar tech multipliers) and complexity factors (high integration vs standalone).
3. Update Size Definitions
Based on actual data, compare old ranges to actual averages and propose new ranges. Update the story point mapping (1, 2, 3, 5, 8 points) accordingly. Flag 8-point stories as requiring split.
4. Refine PROBE Proxies
Update Proxy Story Library:
- Retire outdated proxies that no longer represent current patterns
- Add new proxies from recent well-estimated stories
- Organize proxies into categories: ui_simple, ui_with_api, backend_crud, integration
- Calculate average duration for each category
5. Create Regression Model
If sufficient data (>30 stories), build a regression model with:
- Base overhead (intercept)
- Factors per: acceptance criteria, API endpoints, UI components, database tables, integration points
- Formula combining all factors
- Model accuracy metrics (R-squared, mean error, confidence level)
6. Generate Recommendations
Specific Improvements:
Generate three categories of recommendations:
- Immediate actions: Size definition updates, buffer adjustments, estimate calibrations
- Process improvements: Flags for large stories, checklists for integrations
- Training needs: Team-specific guidance based on identified patterns
7. Update Estimation History
Record calibration details: date, stories analyzed, adjustments made (size ranges, proxy changes, patterns identified), accuracy improvement metrics, and next calibration trigger.
8. Generate Calibration Report
Generate a markdown report including:
- Executive Summary (stories analyzed, overall accuracy, key finding, primary action)
- Size Category Performance table
- Pattern Analysis (overestimation and underestimation patterns with actions)
- Updated Sizing Guide with new ranges
- Recommendations
- Next Steps
See ./reference/probe-methodology.md for the full report template.
Success Criteria
Output
- Calibration report (markdown)
- Updated estimation-history.yaml
- Modified PROBE proxy library
- New size range definitions
- Team communication of changes
