Investigating a manual bug report ("typing petits as the correction
for petit doesn't validate"): both a direct test of
sentence_correction.tscn and a full exercise_host integration test
(mission_02_pont_des_phrases) tap the faulty token, type its exact
expected correction, and submit -- both pass, confirming the
validator and the scene wiring correctly accept "petits" when the
word is tapped once and the correction is typed before pressing
Valider.
Caught along the way: a first draft of the direct-scene test used a
lambda connected to answer_submitted to capture its parameters into
outer local variables -- GDScript closures capture locals by value
at creation time, so reassigning them inside the lambda never
reaches the outer scope. Switched to GUT's
assert_signal_emitted_with_parameters, the supported way to inspect
signal arguments in a test.
No fix applied yet: these tests confirm the intended interaction
works, but don't rule out a real fragility noticed while reading the
code -- toggling a token off deletes its LineEdit (and whatever was
typed in it) immediately, so an accidental second tap on the same
word silently discards the correction. Needs the user's exact click
sequence to confirm before changing behavior.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Wires every autoload up to a real UI: boot -> profile select/create
-> world map (three mission markers, positioned from data, not
hardcoded pixels) -> mission intro (narrative from data/missions/,
a new data type with its own schema) -> rule panel -> exercise host
-> one of the three exercise type scenes -> feedback panel -> reward
screen -> back to the map.
exercise_host is the only piece with real orchestration logic: picks
exercises via ExerciseEngine (now fully implemented, validating at
load like make validate-data does), validates answers through the
pure AnswerValidator/DictationAnalyzer, escalates aid over 3 attempts
via AttemptFlow, feeds DifficultyManager so adaptive difficulty is
actually wired in rather than dead code, and records every attempt
through ProgressManager. Every exercise-type scene (multiple_choice,
sentence_correction, dictation) only emits its answer by signal, per
the architecture invariant.
All child-facing text now comes from data/localization/fr.json via
UIText, not hardcoded in scripts or scenes. Getting that right
required extending SchemaValidator to support "additionalProperties"
as a schema (dynamic keys), which also closed a real gap: save.skills
was previously unvalidated against SkillState's actual shape.
16 new headless tests, including two integration tests that instance
exercise_host.tscn directly and drive it by emitting child-scene
signals, stopping short of the final scene transition (which would
tear down the test runner's own tree). Everything that needs a
screen -- layout at different sizes, touch, virtual keyboard, actual
audio playback (no .ogg files exist yet) -- is listed in the new
docs/testing/manual-checklist.md instead of claimed as verified.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
scripts/profiles/: SaveIO handles real file I/O in user://saves (one
file per profile_id, atomic write via temp-file-then-rename, backup
copy before each overwrite, corrupted-main falls back to backup,
schema-invalid content is rejected like corruption). SaveMigration
and SaveFactory are pure; SaveMigration refuses a schema_version it
doesn't recognize (older or newer) rather than guessing. ProgressUpdates
is pure and wires ReviewScheduler/DifficultyManager onto a save's
skills dict, including per-session overdue-review marking.
The three autoloads (SaveManager, ProfileManager, ProgressManager)
stay thin: in-memory current-profile state and orchestration only,
no logic of their own, so the actual behavior lives in the tested
pure/file-IO modules above rather than in untestable glue.
26 new headless tests, including the failure modes called out in the
launch prompt: a truncated .tmp file never corrupts the readable
save, a corrupted main file falls back to its backup, two profiles
never mix (down through the real autoload layer, not just SaveIO),
and an unknown future schema_version is rejected outright.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
scripts/correction/: AnswerValidator (multiple choice, sentence
correction via tap-the-faulty-word + retype) and DictationAnalyzer
build the CorrectionResult structure from cahier des charges §6.
Dictation analysis aligns words with WordAligner (Wagner-Fischer edit
distance) before any character comparison, so an omitted or added
word doesn't cascade into false errors on every following word.
TextNormalizer handles non-breaking spaces, typographic apostrophes,
and decomposed-vs-composed French accented characters via an
explicit table (GDScript has no built-in NFC/NFD normalization).
Error classification follows the data annotations only (rule_id
present -> grammatical, absent -> unclassified), never a runtime
linguistic guess.
scripts/pedagogy/: DifficultyManager (per-skill difficulty from
recent results), AttemptFlow (3-attempt graduated aid: hint, then
explanation, then reveal, matching exercise-lifecycle.md's second
gap), and ReviewScheduler (the five mastery states from §9, with
session-counted spaced review intervals [1, 3, 6, 12] rather than
wall-clock dates, since a family device's clock isn't trustworthy).
All of it is pure: no scene tree, no clock or RNG read directly,
every effectful input (session number, attempt outcome) is a
parameter. 65 headless tests across both layers, including the
word-omission-doesn't-cascade regression and the full
decouverte -> acquise -> a_reviser -> acquise path.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Introduces a generic JSON-Schema-subset validator in GDScript
(scripts/data/schema_validator.gd) used both by `make validate-data`
and, later, by ExerciseEngine at load time, plus semantic_checks.gd
for cross-field constraints a schema can't express (answer index
bounds, at least one faulty token in sentence correction, word count
matching between a dictation's target_text and its annotations).
Schemas cover rule, exercise (dispatched per type: multiple_choice,
sentence_correction), dictation, curriculum, and save (save.schema.json
is not yet wired into validate-data; saves live in user://, handled
at the Profiles/Saves step).
First draft content for the CE1 "accord_adjectif" rule (one rule, two
multiple-choice exercises, one sentence correction, one dictation,
one curriculum entry) exercises the full pipeline end to end and
seeds docs/review/content-review.md, regenerated by the new
tools/generate_content_review.py (`make content-review`).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Pins Godot 4.6.3-stable and GUT 9.6.0 (9.6.1+ reference
AccessibilityServer, unavailable in 4.6.3) via tools/setup.sh. Wires
make check (gdlint + gdformat config for 4-space indent, headless
GUT run, and a JSON well-formedness check ahead of real schema
validation). Seven autoload stubs match the architecture in the
cahier des charges; real logic lands with the following steps.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Denis Defolie