Evidence-Driven Proportional Allocation

Time is not measured — it is derived

EDPA v1.0.0 replaces manual timesheets with automatic hour derivation from GitHub delivery evidence. Mathematical guarantee, zero administration.

Timesheets

Zero

No manual logging

Guarantee

Sum = Capacity

Mathematically guaranteed

Analytics

Dual-view

Per-person & per-item

Calibration

Self-tuning

Auto-calibration (Karpathy loop)

Context

Why do we need EDPA?

Inaccuracy

Manual timesheets are subjective. People estimate, round, forget. Data does not reflect reality.

Audit nightmare

During audits it is impossible to prove retroactively who worked on what. Evidence is missing, provability is lacking.

Administrative burden

Filling out reports takes time that could go into development. Nobody likes timesheets.

No per-item view

Traditional systems show only personal reports. How much did a specific deliverable cost? Impossible to determine.

Solution

How EDPA works

Score = JobSize × ContributionWeight × RelevanceSignal DerivedHours = (Score / ΣScores) × Capacity Guarantee: Σ DerivedHours = Capacity — always, no exceptions.

The system consists of three separate layers that collaborate to automatically derive working hours.

Operational Metadata

GitHub Issues + Projects

Live delivery data. Hierarchy, status, Job Size, WSJF. Everything the team already uses daily.

Capacity Registry

YAML config in repo

Capacity, roles, FTE. Confirmed at Iteration Planning. The only manual input to the system.

Evidence & Reporting

/snapshots · /reports · /signed

Frozen snapshots, reports, BankID signatures. Immutable and auditable.

Evidence detection

Signals from GitHub

EDPA automatically detects developer contributions from GitHub activities. Each signal has an assigned weight (priority) and corresponding ContributionWeight.

Signal Priority CW Description
Assignee +4 1.0 Primary issue resolver
/contribute +3 0.6 Explicit collaboration sign-up
PR author +2 0.6 Pull request author
Commit +1 0.25 Commit in a relevant branch
PR reviewer +1 0.25 Pull request code review
Comment +0.5 0.15 Comment in issue or PR

Dual-View analytics

Two views, one dataset

Per-Person

How is a person's capacity distributed across items?

DerivedHours = (Score / ΣScores) × Capacity Guarantee: Σ = Capacity

Report for audit

Per-Item

How many people and hours did item X cost?

ItemShare = DerivedHours[P] / Σ DerivedHours[*] Guarantee: Σ shares = 100%

Cost allocation per deliverable

Both views are derived from the same data (CW, JobSize, Capacity). No duplication, no conflict.

Key features

Why EDPA works

Zero manual input

No timesheets. The report is a byproduct of delivery. The team works, the system calculates.

Mathematical guarantee

Σ DerivedHours = Capacity. Always. Proportional allocation ensures consistency.

Dual-view analytics

Per-person reports for reporting. Per-item cost cards for management. One dataset.

Audit-grade (BankID)

Frozen snapshots + BankID signatures. Legally stronger than paper reports.

Self-tuning

Karpathy loop: compare → adjust → repeat. The system automatically calibrates to real data.

GitHub-native

GitHub Actions, branch naming conventions, issue tracking. No external tools.

Explore EDPA

Dashboard, case study, presentation, methodology and evaluation — all in one place.