Feature: Context Analysis Agent Coverage
  As a developer
  I want confidence the ContextAnalysisAgent behaves correctly
  So that context generation remains reliable

# TODO: Uncomment when step definitions are implemented
#   Background:
#     Given the context analysis agent module is importable
#     And I have a mock LLM provider configured for context analysis
#
#   Scenario: Agent initializes with defaults
#     When I create a ContextAnalysisAgent with default parameters
#     Then the context analysis agent should be initialized successfully
#     And the agent should have a chunk_size attribute set to 2000
#     And the agent should have a chunk_overlap attribute set to 200
#     And the context analysis agent should have an llm provider configured
#
#   Scenario: Agent respects custom chunk configuration
#     When I create a ContextAnalysisAgent with chunk_size 800 and chunk_overlap 80
#     Then the agent should have a chunk_size attribute set to 800
#     And the agent should have a chunk_overlap attribute set to 80
#
#   Scenario: Workflow graph contains expected nodes
#     Given I have a ContextAnalysisAgent instance
#     When I inspect the workflow graph
#     Then the graph should contain node "load_files"
#     And the graph should contain node "analyze_dependencies"
#     And the graph should contain node "chunk_documents"
#     And the graph should contain node "score_relevance"
#     And the graph should contain node "summarize_context"
#
#   Scenario: Load files node reads real files
#     Given I have a ContextAnalysisAgent instance
#     And I have a temporary test file named "example.py" with content:
#       """
#       import os
#       print("hi")
#       """
#     When I execute the load_files node with file paths:
#       """
#       ["example.py"]
#       """
#     Then the state should contain documents
#     And the documents list should have 1 documents
#     And the first document should contain "hi"
#     And there should be no error
#
#   Scenario: Load files node reports missing and invalid paths
#     Given I have a ContextAnalysisAgent instance
#     And I have a temporary directory named "invalid"
#     When I execute the load_files node with file paths:
#       """
#       ["missing.py", "invalid"]
#       """
#     Then the state should contain documents
#     And the documents list should have 0 documents
#     And the state error should contain "File not found"
#     And the state error should contain "Not a file"
#
#   Scenario: Dependency analysis returns structured data
#     Given I have a ContextAnalysisAgent instance
#     And I have a state with loaded documents containing:
#       """
#       import os
#       import sys
#       """
#     When I execute the analyze_dependencies node
#     Then the state should contain dependencies
#     And the dependencies should be a dictionary
#
#   Scenario: Chunking splits large documents
#     Given I have a ContextAnalysisAgent instance with chunk_size 50 and chunk_overlap 10
#     And I have a state with a document of 140 characters
#     When I execute the chunk_documents node
#     Then the chunks list should have at least 2 chunks
#
#   Scenario: Relevance scoring produces values per file
#     Given I have a ContextAnalysisAgent instance
#     And I have a state with chunks from 2 different files
#     When I execute the score_relevance node
#     Then the relevance_scores should be a dictionary
#     And the relevance_scores should contain 2 entries
#     And all scores should be between 0.0 and 1.0
#
#   Scenario: Summarization generates a summary
#     Given I have a ContextAnalysisAgent instance
#     And I have a complete analysis state with:
#       | field            | value |
#       | documents        | 3     |
#       | dependencies     | 5     |
#       | relevance_scores | 3     |
#     When I execute the summarize_context node
#     Then the state should contain a summary
#
#   Scenario: ContextAnalysisAgent retries transient summary failures
#     Given I have a ContextAnalysisAgent instance
#     And I have a complete analysis state with:
#       | field            | value |
#       | documents        | 2     |
#       | dependencies     | 2     |
#       | relevance_scores | 2     |
#     When I execute the summarize_context node with a flaky LLM that fails once
#     Then the summarize_context node should succeed after retry
#
#   Scenario: Complete workflow processes files end to end
#     Given I have a ContextAnalysisAgent instance
#     And I have temporary test files:
#       | filename | content                 |
#       | a.py     | import os\nprint("A")   |
#       | b.py     | import sys\nprint("B")  |
#     When I run the complete workflow with file paths:
#       """
#       ["a.py", "b.py"]
#       """
#     Then the workflow should complete successfully
#     And the final state should contain documents
#     And the final state should contain dependencies
#     And the final state should contain relevance_scores
#     And the final state should contain a summary
#
#   Scenario: Async execution matches sync behavior
#     Given I have a ContextAnalysisAgent instance
#     And I have a temporary test file named "async.py" with content "print('x')"
#     When I run the workflow asynchronously with file paths:
#       """
#       ["async.py"]
#       """
#     Then the async workflow should complete successfully
#     And the final state should contain all expected fields
#
#   Scenario: Streaming produces intermediate updates
#     Given I have a ContextAnalysisAgent instance
#     And I have a temporary test file named "stream.py" with content "print('x')"
#     When I stream the workflow with file paths:
#       """
#       ["stream.py"]
#       """
#     Then I should receive multiple state updates
#     And each update should correspond to a node execution
#
#   Scenario: Helper parsing handles structured dependency output
#     Given I have a ContextAnalysisAgent instance
#     When I parse dependencies from:
#       """
#       Dependencies: ['os', 'sys', 'pathlib']
#       """
#     Then the parsed dependencies should include "os"
#     And the parsed dependencies should include "sys"
#
#   Scenario: Dependency analysis reports chained errors
#     Given I have a ContextAnalysisAgent instance with an LLM that raises "dependency failure"
#     And I have a state with loaded documents containing:
#       """
#       import pathlib
#       """
#     And the state error is "load failure"
#     When I execute the analyze_dependencies node
#     Then the state should contain dependencies
#     And the dependencies for "test.py" should be empty
#     And the state error should contain "load failure"
#     And the state error should contain "dependency failure"
#
#   Scenario: Relevance scoring skips duplicate chunks per file
#     Given I have a ContextAnalysisAgent instance
#     And I have a state with duplicate chunks from "dup.py"
#     When I execute the score_relevance node
#     Then the relevance_scores should be a dictionary
#     And the relevance_scores should contain 1 entries
#     And all scores should be between 0.0 and 1.0
#
#   Scenario: Relevance scoring reports LLM errors and preserves prior issues
#     Given I have a ContextAnalysisAgent instance with an LLM that raises "relevance failure"
#     And I have a state with chunks from 1 different files
#     And the state error is "previous issue"
#     When I execute the score_relevance node
#     Then the relevance_scores should be a dictionary
#     And the relevance_scores should contain 1 entries
#     And the state error should contain "previous issue"
#     And the state error should contain "relevance failure"
#
#   Scenario: Relevance parser handles qualitative hints
#     Given I have a ContextAnalysisAgent instance
#     When I parse relevance scores from hints:
#       | hint                      | expected |
#       | High likelihood of use    | 0.8      |
#       | low confidence in result  | 0.3      |
#       | outcome undecided         | 0.5      |
#     Then the parsed scores should match expected values
#
#   Scenario: Summarization merges prior errors when LLM fails
#     Given I have a ContextAnalysisAgent instance with an LLM that raises "summary failure"
#     And I have a complete analysis state with:
#       | field            | value |
#       | documents        | 2     |
#       | dependencies     | 2     |
#       | relevance_scores | 2     |
#     And the state error is "previous pipeline error"
#     When I execute the summarize_context node
#     Then the state should contain a summary
#     And the summary should equal "Context analysis failed"
#     And the state error should contain "summary failure"
#     And the state error should contain "previous pipeline error"
#
#   Scenario: Async streaming produces node updates
#     Given I have a ContextAnalysisAgent instance
#     And I have a temporary test file named "astream.py" with content "print('stream')"
#     When I stream the workflow asynchronously with file paths:
#       """
#       ["astream.py"]
#       """
#     Then I should receive multiple state updates
#     And each update should correspond to a node execution