Two critical test additions:
1. test_system_full_lifecycle.py — Full A-to-Z integration test:
Exercises the ENTIRE bot pipeline across 4 screen types (HomeFeed →
Explore → Post Detail → Profile) with a real InstagramEmulator state
machine. Validates plugin execution, session state accumulation,
cognitive stack survival, dopamine session timeout, and post-session
serialization. This is the single most important test in the suite.
2. test_system_coverage_gate.py — Critical Path Coverage Enforcement:
Reads coverage_e2e.json and enforces minimum coverage thresholds on
production-critical modules. Each threshold is tied to a real production
incident. If persistent_list.py drops below 20% coverage, the build
fails — because that's exactly how the sessions.json corruption
bug went undetected.
Usage: pytest tests/e2e --cov=GramAddict.core \
--cov-report=json:coverage_e2e.json
Critical paths enforced:
- session_state.py (>=50%): serialization crash prevention
- persistent_list.py (>=20%): persist() path must be tested
- dopamine_engine.py (>=40%): session timeout logic
- screen_identity.py (>=60%): screen identification
- spatial_parser.py (>=70%): UI element parsing
- intent_resolver.py (>=50%): click decision logic
- q_nav_graph.py (>=25%): navigation integrity
- device_facade.py (>=40%): device interface
Replaces reactive bug-specific tests with 4 structural CONTRACTS
that make entire categories of bugs impossible:
1. Serialization Contract (13 parametrized hostile payloads):
Proves SessionStateEncoder NEVER crashes regardless of what
gets injected onto args (datetime, lambda, bytes, inf, nan, etc.)
2. Persistence Contract:
Forces the REAL persist() path by removing PYTEST_CURRENT_TEST guard.
Validates write → read round-trip with poisoned sessions.
3. Production Parity Contract:
Scans ALL GramAddict source for pytest/test-mode divergence guards.
Any unaudited divergence point fails the build. New guards require
explicit justification in KNOWN_DIVERGENCES registry.
4. Import Integrity Contract:
Imports every single GramAddict module to catch syntax errors,
circular imports, and missing dependencies at test time.
This addresses the root systemic failure: tests were running in a
parallel universe where serialization was silently skipped, allowing
a datetime injection to corrupt sessions.json and kill production.
Root cause: bot_flow.py injected configs.args.global_start_time = datetime.now()
which polluted the args namespace. SessionStateEncoder blindly serialized
args.__dict__ via json.dump, which crashed mid-write on the datetime object,
leaving sessions.json truncated/corrupt. Every subsequent restart failed.
Fixes:
- Remove global_start_time from configs.args (only lives on engine class vars)
- Harden SessionStateEncoder with _sanitize_value() to convert any
non-JSON-serializable type (datetime, timedelta, arbitrary objects) to strings
- Add test_system_session_persistence.py with 4 tests covering the exact
production crash scenario (datetime injection → json.dumps → round-trip)
- Fix test_engine_timeout.py broken GoalExecutor module reference
Bug 1 — VLM Profile Tab Hallucination:
- VLM confused 'Profile' nav tab with post author username
- Added Author Tab Guard to filter_navigation_conflicts()
- Fixed mock LLM to use structural row_feed_photo_profile matching
instead of hardcoded username (was the test lie)
Bug 2 — Like Button 1-Byte Delta Kill:
- Toggle actions (like/save) produce 1-byte XML deltas
- GOAP's MIN_UI_CHANGE_BYTES=50 threshold killed them as 'no change'
- Split interaction gate: interactions use ANY xml diff, navigations
keep the 50-byte threshold
Bug 3 — Empty Username Silently Accepted:
- PostDataExtraction returned 'Post by @' with no warning
- Added 'username_missing' reliability flag to result dict
- Downstream consumers can now detect degraded data quality
Cleanup:
- Removed all debug print() statements from production code
- Replaced with structured logger.debug() calls
PRODUCTION FIX 2026-04-30 12:45:
The entire E2E suite was systematically lying by testing a shell of the bot:
1. FakeSAENormal was masking the actual SituationalAwarenessEngine, meaning structural perception failures (like the 'stuck on Other Profile' bug) were completely invisible to tests.
2. The e2e_cognitive_stack was hardcoded to return None for 80% of engines (NavGraph, ZeroEngine, Telepathic, Darwin, ActiveInference), causing plugins to silently bypass their core logic during tests.
3. get_screenshot_b64 and screenshot() returned None, preventing the VLM stack from even running structural fallback code.
Fix:
- Systematically stripped FakeSAENormal from all 29 E2E workflow tests.
- Built e2e_cognitive_stack_factory to instantiate the REAL Cognitive Stack (GrowthBrain, QNavGraph, ZeroLatencyEngine, etc.) just like in bot_flow.py.
- Patched ONLY the external LLM network requests in conftest.py via mock_llm_network_calls so that structural execution stays 100% real but avoids non-deterministic LLM timeouts.
- Injected a valid 1x1 black image for screenshots.
All 29 E2E tests now execute the true production logic path and pass in 49 seconds.
PRODUCTION BUG 2026-04-30 11:50:
VLM returned action_bar_button_back (desc='Back') for 'tap profile tab'
intent on OTHER_PROFILE screen. This caused account_switcher to navigate
AWAY from the profile instead of opening the account selector bottom
sheet, halting the session.
Root cause: _visual_discovery pre-filter did not exclude navigation
buttons (Back, Close) when the intent was for a tab element.
Fix:
- Added filter_navigation_conflicts() method to IntentResolver
- Guards tab intents by excluding nodes with 'back' in resource_id
or content_desc == 'Back'
- Does NOT apply for 'tap back button' or 'press back' intents
TDD:
- RED: test_intent_resolver_back_guard.py — 2 tests that call
filter_navigation_conflicts on real OTHER_PROFILE XML
- GREEN: filter_navigation_conflicts method + wired into _visual_discovery
- E2E: test_workflow_account_switch.py, test_workflow_vlm_tab_confusion.py
31/31 tests passing.
BEFORE: Giant monolith test files with duplicated device stubs, config
factories, and cognitive stack builders. The infrastructure was copy-pasted
into each file.
AFTER: Clean separation:
- conftest.py: E2EDeviceStub, e2e_device, e2e_cognitive_stack,
e2e_session, e2e_workflow_ctx fixtures
- test_workflow_permission_dialog.py: Permission dialog recovery
- test_workflow_foreign_app.py: Foreign app (Chrome) recovery
- test_workflow_normal_feed.py: Normal post processing
- test_workflow_plugin_integrity.py: Import + interface sanity
Each workflow file is <100 lines. Zero duplication. Every test uses
the shared e2e_workflow_ctx fixture which runs the EXACT code path
from bot_flow.py:942-971.
DELETED: test_full_workflow_hostile_env.py, test_plugin_chain_hostile_env.py
(monolith files replaced by the above)
PROBLEM:
All 81 existing E2E tests were disguised unit tests. They tested individual
components (IntentResolver, SpatialParser, SAE perception, single plugins)
in isolation. NONE ran the actual production workflow.
This meant:
- A broken import in resonance_evaluator? No test caught it.
- obstacle_guard ignoring OBSTACLE_SYSTEM? No test caught it.
- The full plugin chain running against a permission dialog? Never tested.
THE FIX — test_full_workflow_hostile_env.py:
These tests mock ONLY the Android device (XML dumps). Everything else is real:
- Real DopamineEngine
- Real SessionState
- Real Config
- Real PluginRegistry with ALL 18+ production plugins
- Real BehaviorContext built exactly like bot_flow.py:942-954
Tests:
1. test_feed_loop_iteration_recovers_from_permission_dialog
→ Runs the EXACT code path from bot_flow.py against a permission dialog
→ Asserts should_skip=True, zero interactions, BACK pressed
2. test_normal_feed_post_is_processable
→ Sanity check that the full pipeline processes normal posts
3. test_all_plugins_importable_and_instantiable
→ Catches broken imports (humanized_scroll bug) at registration time
4. test_plugin_chain_does_not_swallow_import_errors
→ Explicit import of every plugin module — any ImportError = test failure
RULE CODIFIED: E2E = mock ONLY Instagram. Everything else is REAL.
THE ROOT CAUSE OF LYING TESTS:
81 E2E tests existed but ZERO tested the full plugin chain (execute_all)
against non-Instagram environments. Each plugin was tested in isolation,
but the integration between perception (SAE) and action (obstacle_guard)
was a complete blind spot.
NEW TESTS:
- test_chain_terminates_on_permission_dialog: Proves the full plugin chain
terminates at obstacle_guard when a system permission dialog is detected.
No downstream interaction plugins (resonance, like, follow) may fire.
- test_chain_terminates_on_foreign_app: Same for Chrome/browser takeover.
- test_chain_passes_through_on_normal_instagram: Sanity check that the
chain runs normally on valid Instagram feeds.
These tests would have caught the 23:01 stuck-loop bug BEFORE it shipped.
- Resolved Bug #5: Fixed list vs str parsing in ResonanceEvaluator
- Resolved Bug #6: Use 'should_like' key for vibe score
- Resolved Bug #7: Guard TelepathicEngine against 'Follow' nodes for post media
- Resolved Bug #8: Implemented failed_bounds exclusion loop breaker in PerfectSnapping
- Resolved Bug #10: Corrected available_actions string parsing
- Validated with E2E regression suite (100% green)
🔴 RED → 🟢 GREEN for 4 critical bugs found in production run 2026-04-29:
1. ResonanceEvaluator: Add null-guard for evaluate_post_vibe() return.
When VLM returns truncated JSON, the function returns None. The caller
now handles this gracefully instead of crashing with AttributeError.
2. ScreenIdentity POST_DETAIL: Replace broken 'and not selected_tab'
condition with structural differentiator using main_feed_action_bar.
Posts opened from feed retain feed_tab selected, which was causing
misclassification as HOME_FEED → LLM fallback → OWN_PROFILE hallucination
→ permanent Qdrant cache poisoning.
3. ActionMemory VLM verification: When VLM returns JSON instead of YES/NO,
treat as inconclusive (fall through to structural delta) rather than
hard failure. Only return False when response explicitly contains 'no'.
4 new E2E regression tests, 75/75 pass, zero regressions.
Removed the hardcoded structural fallback bypasses for bottom navigation tabs to ensure 100% autonomous visual inference. Expanded the VLM intent resolution prompt with explicit spatial heuristics for bottom navigation (e.g., 'profile tab is the avatar icon at the bottom right') to prevent LLaVA hallucinations without resorting to XML resource-id hacks. Added E2E visual test proof.
Added a 'Structural Navigation Guard' in IntentResolver to map critical bottom navigation intents (e.g., 'tap profile tab') directly to their stable resource-ids. This bypasses the VLM entirely, guaranteeing 100% deterministic clicks and resolving the issue where the VLM failed to locate the profile tab, causing the edge to become masked and trapping the bot on the home feed.
Included TDD proof.
Replaced weak 'y_center < 2000' and 'not action_bar' assertions with hard structural and semantic validations for author username clicks.
We now explicitly verify that the VLM selected the correct resource-id or matching text/content-desc.
In addition to prompt tuning, we now apply a pre-flight structural guard for 'tap first post' / 'tap first grid item' intents.
If the intent targets a grid item, we pre-filter the candidates to only include those matching 'row X, column Y', 'photos by', or 'reel by'.
This drastically narrows the Set-of-Mark candidates down to ONLY valid grid items, making it literally impossible for the VLM to hallucinate and click navigation elements like 'Search' or 'Home' when asked to tap a post.
Also updated E2E test to enforce strict post-click assertion, preventing lying tests.
ROOT CAUSE: qwen3.5 (reasoning model) returns response='' with thinking
block containing all reasoning. llm_provider.py line 352 silently
substituted the thinking block as the response via:
content = raw_response or raw_thinking or ''
The Brain then extracted random actions from the reasoning text.
FIXES:
1. llm_provider.py: Conditional thinking isolation
- format_json=True (SAE/perception): thinking fallback preserved
- format_json=False (Brain): thinking NEVER substituted
- Added think=false for Ollama free-text calls to force direct response
2. planner.py: No-Op Guard strips tab actions that navigate to
the current screen (e.g. 'tap profile tab' on OWN_PROFILE)
3. test_brain_live.py: Stochastic testing (5 runs, 60% min valid)
to handle non-deterministic LLM behavior reliably
4. tests/integration/test_llm_provider_pipeline.py: NEW test layer
mocking at HTTP level (requests.post) to exercise the FULL
llm_provider → Brain pipeline. This would have caught the
thinking substitution bug from day one.
Suite: 168 passed, 0 failed
The central autonomous brain (GoalExecutor.achieve()) had ZERO E2E coverage.
The deleted lying test_e2e_autonomous_session.py never called it at all,
allowing the AttributeError and dead code bugs to survive undetected.
New tests exercise the REAL achieve() with production XML fixture sequences:
- Navigation: HOME_FEED → tap explore tab → EXPLORE_GRID (HD Map routing)
- Already-on-target recognition (0-step achievement)
- max_steps exhaustion → returns False (anti-infinite-loop)
- Return type contract enforcement (bool, not string)
All 4 tests use make_real_device_with_xml with real fixture sequences.
No mocks. No patches. No lies.
E2E: 60 passed, 5 skipped, 0 failures.
CRITICAL LIES FIXED:
- bot_flow.py:474 compared achieve() (returns bool) to 'GOAL_ACHIEVED'
(string). Success path was dead code — True never == string.
- TestBotFlowDMGating built its own local target_map dict and asserted
against it. bot_flow.py no longer has target_map (uses GoalExecutor).
Tests verified their own imagination, not production code.
- test_perception_mock_theater_purged was a skip+pass ghost creating
false 'skipped' coverage in reports.
- test_perceive_notification_shade silently passed on FileNotFoundError
instead of reporting the missing fixture.
- test_resolve_uses_visual_discovery_when_device_available only checked
hasattr — verifying method existence, not behavior.
PRODUCTION BUGS FIXED:
- GoalExecutor constructor called with wrong args (memory, telepathic,
config, session_state) — it only accepts (device, bot_username).
- achieve() result comparison was dead code: always hit warning branch.
E2E: 57 passed, 4 skipped (live_llm waivers), 0 failures.
Two root causes for 'scroll on story' bug:
1. ScreenIdentity had ZERO structural markers for story views.
reel_viewer_media_layout, reel_viewer_header, reel_viewer_progress_bar
and content-desc 'Like Story'/'Send story' now → STORY_VIEW.
2. Brain prompt was prescriptive ('you MUST scroll down'), overriding
the LLM's intelligence. Rewritten to give context about screen types
and let the AI reason autonomously about which action makes sense.
Philosophy: AI decides navigation, we provide correct perception data.
No hardcoded 'if story → press back' escape hatch.
4 new perception tests (all green), 0 regressions.
Bug evidence from run 2026-04-27_23-46-57:
- Bot started on a story (reel_viewer_media_layout, 'Like Story')
- ScreenIdentity classified it as UNKNOWN
- GOAP chose 'scroll down' 4 times (stories don't scroll)
- Bot was trapped in infinite scroll loop
Captured real XML fixture: story_view_full.xml
1 test FAILS (screen_identity → UNKNOWN instead of STORY_VIEW)
Kill-Switch: Refuse DM processing when dm_reply.enabled=false in config.
Root cause: checked nonexistent 'disable_ai_messaging' flag instead of
actual plugin config.
Context Guard: Skip threads with no extractable message text.
Root cause: LLM was fed 'No previous context' → produced garbage like
'the to the'.
Send Verification: Structurally verify Send button resource-id/desc.
Root cause: VLM returned reactions_pill_container, edit fields, etc.
and engine blindly clicked them, logging 'Successfully sent'.
Iteration Cap: MAX_REPLIES_PER_INBOX_VISIT=3 prevents spam.
Root cause: no loop guard → 8 DMs sent in 2 minutes in production.
Refactored: removed dead 'if True' guard, de-indented block,
moved dm_memory.log_sent_dm into success branch only.
All 6 E2E tests pass. No regressions (54/55 passed, 1 pre-existing).
Tests expose:
1. DM Engine ignores dm_reply.enabled config (checks nonexistent 'disable_ai_messaging')
2. Logs 'Successfully sent' without verifying actual Send button click
3. Generates garbage replies from 'No previous context' (story replies)
4. No max-iteration guard — sent 20 messages in test, 8 in production
All 4 tests FAIL. Ready for GREEN phase.
TDD RED Phase: These tests PROVE the gaps that allowed the production bug
where the bot logged 'Followed @missiongreenenergy ✓' after clicking a photo grid item.
5 RED tests expose:
1. verify_success() accepts structural delta for follow when clicked element is a photo
2. verify_success() accepts 500-char delta without semantic match check
3. QNavGraph.do() missing 'follow' in action_checks screen-sanity map
4. ActionMemory.confirm_click() poisons Qdrant with mismatched intent→element
5. GOAP._execute_action() clicks first without pre-click sanity check
All 5 tests FAIL (RED) as expected — proving the lies in the current test suite.
No production code was changed.
BREAKING: IntentResolver now resolves intents by SEEING the screenshot
instead of parsing XML text descriptions.
Architecture:
- PRIMARY: Visual Discovery (SoM) — annotates screenshot with numbered
bounding boxes, sends to VLM, VLM visually picks the right box
- FALLBACK: Text-based VLM resolution (only when no device available)
- Removed: _visual_critic (redundant — visual discovery IS visual)
- Removed: _humanize_desc regex (the VLM reads the actual screen now)
Key innovations:
- Spatial Deduplication: child nodes fully contained in parent bounds
are suppressed (83 → ~19 boxes), eliminating visual noise
- System UI filtering: statusbar, notifications excluded from candidates
- VLM prompt is pure visual: 'look at the numbered boxes and pick one'
Proven by live LLM test: VLM correctly identifies 'following' (not
'followers') by SEEING the screen content, with zero string matching.
