forked from cleveragents/cleveragents-core
freemo
a808c395f9
Add 53 new .feature files and corresponding step definition files targeting uncovered lines identified in build/coverage.xml. Fix AmbiguousStep conflicts in 7 pre-existing step files by disambiguating step text. New tests cover: ACP clients/facade, actor CLI/config, application container, ACMS service/strategies, async worker, automation profile CLI, autonomy guardrail, bridge, change model, config CLI/service, context service, cross-plan correction, database models, decision service, decomposition clustering/service, discovery handler, langchain chat provider, langgraph nodes, materializers, multi-project service, plan apply/CLI/lifecycle/model/ preflight/resume/service, PostgreSQL analyzer, project CLI/context CLI, provider registry, reactive application/route, repositories, resolver handler, resource registry service, resume model, retry patterns, sandbox protocol, server CLI, skill CLI/service, skills registry, subplan execution/service, system CLI, UKO loader, UoW, and YAML template engine. Closes #645
74 lines
3.3 KiB
Gherkin
74 lines
3.3 KiB
Gherkin
Feature: LangChain chat provider coverage boost
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As a maintainer focused on test coverage completeness
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I want Behave scenarios that exercise remaining uncovered paths
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So that langchain_chat_provider.py reaches full line coverage
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Background:
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Given a fresh LangChain chat provider with mocked dependencies
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@coverage @langchain @token_cost
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Scenario: Token cost resolution returns None when float conversion fails
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When the usage tracker reports a non-numeric cost value
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Then resolving token cost should return None
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@coverage @langchain @extract_event
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Scenario: Extract event returns unknown sentinel for empty event dict
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When an empty event dictionary is passed to extract_event
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Then the extracted node name should be "__unknown__" with an empty payload
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@coverage @langchain @token_estimate
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Scenario: Token estimation returns zero when the estimator callable raises
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When the LLM token estimator raises an exception during estimation
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Then the estimated token count should be zero
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@coverage @langchain @token_estimate
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Scenario: Token estimation returns zero when the result is not int-convertible
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When the LLM token estimator returns a value that cannot be cast to int
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Then the estimated token count should be zero
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@coverage @langchain @error_extraction
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Scenario: Error extraction returns None for an empty list
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When the error value is an empty list
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Then the extracted error message should be None
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@coverage @langchain @error_extraction
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Scenario: Error extraction returns None for an empty tuple
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When the error value is an empty tuple
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Then the extracted error message should be None
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@coverage @langchain @error_extraction
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Scenario: Error extraction returns None for an empty set
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When the error value is an empty set
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Then the extracted error message should be None
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@coverage @langchain @error_extraction
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Scenario: Error extraction returns None for a list of only None values
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When the error value is a list containing only None entries
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Then the extracted error message should be None
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@coverage @langchain @callback_discovery
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Scenario: OpenAI callback is discovered from langchain.callbacks module
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When the langchain callbacks module exposes get_openai_callback
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Then the callback discovery logic should bind the callable
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@coverage @langchain @callback_discovery
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Scenario: OpenAI callback discovery skips when attribute is not callable
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When the langchain callbacks module exposes a non-callable get_openai_callback
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Then the callback discovery logic should not bind any callable
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@coverage @langchain @token_cost
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Scenario: Token cost resolution returns None when cost attribute is missing
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When the usage tracker has no total_cost attribute
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Then resolving token cost should return None
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@coverage @langchain @token_estimate
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Scenario: Token estimation aggregates prompt and context content
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When the LLM token estimator counts tokens for plan and multiple contexts
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Then the estimated token count should reflect the combined prompt text
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@coverage @langchain @generate @exception
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Scenario: Generate changes returns error response when workflow raises
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When generate_changes is called and the workflow raises an exception
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Then the response should contain the exception message with zero changes
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And progress should be reported as complete despite the failure
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