MIL-002 Game core logic
Metadata
| Key |
Value |
| ID |
MIL-002 |
| CrossReference |
BC-001 |
| Language |
en |
| Domain |
it |
Version History
| Date |
Status |
Author |
Reviewer |
Change |
Commit |
| 2026-10-13 |
Accepted |
Claude (AI) for S01 |
AI as stand-in for S01 |
Initial version; review RC-004: Go |
pending |
Purpose
Decide whether the game rules work and are tested without a display: loading states, checking guesses, finding missing states and writing the learning file.
Deliverable
A display-free module with the game logic and pytest tests, plus an Architecture Decision Record (ADR) for the data library choice.
Go / No-Go Criteria
| # |
Criterion (objectively checkable) |
Go |
No-Go |
| 1 |
pytest passes with tests for load, guess matching, missing states and CSV export |
Met |
Not met |
| 2 |
Guess matching is case-insensitive and ignores surrounding spaces (ohio matches Ohio) |
Met |
Not met |
| 3 |
states_to_learn.csv lists exactly the 50 states minus those guessed, in one column state |
Met |
Not met |
| 4 |
The logic imports no turtle module |
Met |
Not met |
| 5 |
The ADR for the data library is Accepted |
Met |
Not met |
Dependencies
| Depends on |
Reason |
| MIL-001 |
Layout, tooling and assets must exist |
Traceability
| Business Case objective / KPI / user story |
Reference |
| Objectives 1, 2, 3; Success criteria 1, 2, 6 |
BC-001 |
Ownership
| Role |
Stakeholder ID (SA) |
| Owner |
S01 |
| Approving reviewer |
S01 |
Target Date
2026-10-13 - within the one-working-day duration constraint of BC-001.
Tasks
| # |
Task |
Summary |
Needs its own Use Case/User Story? |
Reference |
| 1 |
Record ADR: csv module versus pandas |
Decide whether to read 50_states.csv with the standard library or pandas. S03 wants no runtime dependencies, S02 follows a pandas lecture. Record the decision and its consequences as an ADR. |
No |
|
| 2 |
Load states data |
Read 50_states.csv into a structure keyed by state name with x and y, using paths and column names from constants.py. |
No |
|
| 3 |
Check a guess |
Match a typed answer to a state case-insensitively and return its coordinates, so the UI can write the name at that place (lecture 2). |
No |
|
| 4 |
Find missing states and write states_to_learn.csv |
Compute missing_states from all states and guessed_states and save them as states_to_learn.csv (lecture 3). |
No |
|
| 5 |
Add pytest tests for the game core |
Tests for load, guess, missing states and CSV output using a temporary directory; they must run without a display. |
No |
|
