Files
cleveragents-core/features/llm_actors_coverage.feature
freemo 051ee7c290 test(coverage): add Behave BDD tests to improve coverage across 52 source files
Added 52 new .feature files and corresponding _steps.py files targeting
previously uncovered code paths in the following areas:

- TUI layer: app, commands, persona (state/schema/registry), widgets,
  input (shell_exec, reference_parser)
- Application services: plan lifecycle/service/executor, session,
  project, repo indexing, correction, checkpoint, actor, llm_actors,
  strategy coordinator, resource file watcher, service retry wiring
- CLI commands: session, resource, repl, plan, db, automation_profile
- Domain models: retry_policy, resource_type, cost_budget,
  docker_compose_analyzer, detail_level, _sql_string_aware,
  _postgresql_helpers
- Core: circuit_breaker, retry_service_patterns
- Infrastructure: repositories, transaction_sandbox, strategy_registry,
  plugins/loader, container
- Config: settings
- Agents: plan_generation, context_analysis, auto_debug
- A2A: facade

All new tests follow the Behave/Gherkin BDD standard. Resolved step
definition collisions with unique prefixes. Fixed Alembic fileConfig
logger disabling issue (disable_existing_loggers=False).

ISSUES CLOSED: #1068
2026-03-20 21:22:10 +00:00

206 lines
9.4 KiB
Gherkin

Feature: LLM Actors Coverage
Exercises previously uncovered code paths in the llm_actors module,
including _parse_actor_name, LLMStrategizeActor, and LLMExecuteActor.
# ---------------------------------------------------------------
# _parse_actor_name helper
# ---------------------------------------------------------------
Scenario: Parse actor name with provider/model format
When I parse actor name "openai/gpt-4"
Then the parsed provider should be "openai"
And the parsed model should be "gpt-4"
Scenario: Parse actor name with only model (no slash)
When I parse actor name "gpt-4"
Then the parsed provider should be "openai"
And the parsed model should be "gpt-4"
Scenario: Parse actor name with empty string defaults to openai/gpt-4
When I parse actor name ""
Then the parsed provider should be "openai"
And the parsed model should be "gpt-4"
Scenario: Parse actor name with anthropic provider
When I parse actor name "anthropic/claude-3"
Then the parsed provider should be "anthropic"
And the parsed model should be "claude-3"
# ---------------------------------------------------------------
# LLMStrategizeActor.__init__ validation
# ---------------------------------------------------------------
Scenario: LLMStrategizeActor rejects None provider_registry
When I create an LLMStrategizeActor with None provider_registry
Then lacov a ValidationError should be raised with message "provider_registry must not be None"
Scenario: LLMStrategizeActor rejects None lifecycle_service
When I create an LLMStrategizeActor with None lifecycle_service
Then lacov a ValidationError should be raised with message "lifecycle_service must not be None"
Scenario: LLMStrategizeActor initializes with valid dependencies
Given a mock provider registry for strategize
And a mock lifecycle service for strategize
When I create an LLMStrategizeActor with valid dependencies
Then the LLMStrategizeActor should be created successfully
# ---------------------------------------------------------------
# LLMStrategizeActor.execute
# ---------------------------------------------------------------
Scenario: LLMStrategizeActor rejects empty plan_id
Given a valid LLMStrategizeActor
When I call strategize execute with empty plan_id
Then lacov a ValidationError should be raised with message "plan_id must not be empty"
Scenario: LLMStrategizeActor executes with stream callback
Given a valid LLMStrategizeActor
And the LLM returns a numbered step list for strategize
When I call strategize execute with plan_id "PLAN123" and a stream callback
Then the strategize result should contain decisions
And the stream callback should have received "strategize_started"
And the stream callback should have received "strategize_decisions"
And the stream callback should have received "strategize_complete"
Scenario: LLMStrategizeActor executes without stream callback
Given a valid LLMStrategizeActor
And the LLM returns a numbered step list for strategize
When I call strategize execute with plan_id "PLAN456" and no stream callback
Then the strategize result should contain decisions
Scenario: LLMStrategizeActor executes with invariants
Given a valid LLMStrategizeActor
And the LLM returns a numbered step list for strategize
When I call strategize execute with plan_id "PLAN789" and invariants
Then the strategize result should contain invariant records
Scenario: LLMStrategizeActor executes with None definition_of_done
Given a valid LLMStrategizeActor
And the LLM returns a numbered step list for strategize
When I call strategize execute with plan_id "PLANX" and None definition_of_done
Then the strategize result should contain decisions
# ---------------------------------------------------------------
# LLMStrategizeActor._parse_decisions
# ---------------------------------------------------------------
Scenario: Parse decisions from numbered list
When I parse LLM decisions from numbered list "1. Create file\n2. Add tests\n3. Run CI"
Then I should get 3 parsed decisions
And parsed decision 0 should be "Create file"
Scenario: Parse decisions from bullet list
When I parse LLM decisions from bullet list "- Create file\n* Add tests\n Run CI"
Then I should get 3 parsed decisions
Scenario: Parse decisions from empty string
When I parse LLM decisions from empty string
Then I should get the default decision "Complete the plan objectives"
Scenario: Parse decisions strips blank lines
When I parse LLM decisions from text with blank lines
Then blank lines should be skipped in the result
Scenario: Parse decisions with numbered colon prefix
When I parse LLM decisions from "1: Create file\n2: Add tests"
Then I should get 2 parsed decisions
And parsed decision 0 should be "Create file"
# ---------------------------------------------------------------
# LLMExecuteActor.__init__ validation
# ---------------------------------------------------------------
Scenario: LLMExecuteActor rejects None provider_registry
When I create an LLMExecuteActor with None provider_registry
Then lacov a ValidationError should be raised with message "provider_registry must not be None"
Scenario: LLMExecuteActor rejects None lifecycle_service
When I create an LLMExecuteActor with None lifecycle_service
Then lacov a ValidationError should be raised with message "lifecycle_service must not be None"
Scenario: LLMExecuteActor initializes with valid dependencies
Given a mock provider registry for execute
And a mock lifecycle service for execute
When I create an LLMExecuteActor with valid dependencies
Then the LLMExecuteActor should be created successfully
# ---------------------------------------------------------------
# LLMExecuteActor.execute
# ---------------------------------------------------------------
Scenario: LLMExecuteActor rejects empty plan_id
Given a valid LLMExecuteActor
When I call execute actor with empty plan_id
Then lacov a ValidationError should be raised with message "plan_id must not be empty"
Scenario: LLMExecuteActor executes with stream callback
Given a valid LLMExecuteActor
And the LLM returns file blocks for execute
When I call execute actor with plan_id "EXEC01" and a stream callback
Then the execute result should contain a changeset
And the execute stream callback should have received "execute_started"
And the execute stream callback should have received "execute_step"
And the execute stream callback should have received "execute_complete"
Scenario: LLMExecuteActor executes without stream callback
Given a valid LLMExecuteActor
And the LLM returns file blocks for execute
When I call execute actor with plan_id "EXEC02" and no stream callback
Then the execute result should contain a changeset
Scenario: LLMExecuteActor executes with sandbox_root
Given a valid LLMExecuteActor
And the LLM returns file blocks for execute
When I call execute actor with plan_id "EXEC03" and a sandbox root
Then the execute result should have sandbox refs
Scenario: LLMExecuteActor executes with sandbox_root in read_only mode
Given a valid LLMExecuteActor
And the LLM returns file blocks for execute
When I call execute actor with plan_id "EXEC04" sandbox root and read_only
Then the execute result should have sandbox refs
And no files should be written to sandbox
Scenario: LLMExecuteActor executes with no file blocks from LLM
Given a valid LLMExecuteActor
And the LLM returns empty response for execute
When I call execute actor with plan_id "EXEC05" and no stream callback
Then the execute result should have zero entries
# ---------------------------------------------------------------
# LLMExecuteActor._parse_file_blocks
# ---------------------------------------------------------------
Scenario: Parse file blocks from LLM output with FILE markers
When I parse file blocks from LLM output with two files
Then I should get 2 changeset entries
And changeset entry 0 path should be "src/main.py"
Scenario: Parse file blocks from LLM output with no files
When I parse file blocks from empty LLM output
Then I should get 0 changeset entries
# ---------------------------------------------------------------
# LLMExecuteActor._write_to_sandbox
# ---------------------------------------------------------------
Scenario: Write to sandbox creates files in the directory
Given lacov a temporary sandbox directory
When I write generated files to the sandbox
Then the sandbox should contain the generated files
Scenario: Write to sandbox handles OSError gracefully
Given lacov a temporary sandbox directory that is read-only
When I write generated files to the read-only sandbox
Then the write should not raise an exception
# ---------------------------------------------------------------
# LLM response without .content attribute
# ---------------------------------------------------------------
Scenario: Strategize actor handles LLM response without content attribute
Given a valid LLMStrategizeActor
And the LLM returns a response without content attribute
When I call strategize execute with plan_id "NOCONTENT" and no stream callback
Then the strategize result should contain decisions