ISEA: Intelligent Software Engineering Agent

# Neo4j Node Types and Relationships

Nodes: Class, Method, Variable, Test

Relationships:
  • BELONGS_TO: Method/Variable → Class
  • CALLS: Method → Method
  • HAS_METHOD: Class ↔ Method
  • HAS_VARIABLE: Class ↔ Variable
  • INHERITS: Class → Class
  • REFERENCES: Method → Variable/Class
  • TESTED: Method → Test

# ISEA Complete Workflow Pseudocode

## Phase 1: System Initialization
INITIALIZE multi_agent_system
SET precise_llm = LLM(model=CLAUDE_SONNET, temperature=0.0)
SET creative_llm = LLM(model=CLAUDE_SONNET, temperature=0.8)

CREATE Agent_Locator(tools=NEO4J_TOOLS + FILE_TOOLS)
CREATE Agent_Suggester(tools=NEO4J_TOOLS + FILE_TOOLS)
CREATE Agent_Fixer(tools=NEO4J_TOOLS + FILE_TOOLS)

## Phase 2: Multi-Agent Workflow Execution
INITIALIZE workflow_graph = StateGraph(AgentState)
ADD_NODES(Locator, Suggester, Fixer, ToolNodes, Summarizer)
ADD_CONDITIONAL_EDGES(routing_logic)
COMPILE workflow_graph

EXECUTE workflow_graph.stream(initial_state)
    → Locator identifies ≤5 issue locations
    → Suggester analyzes context and proposes repair strategies
    → Fixer generates coordinated patches

## Phase 3: Multi-Variant Patch Generation
FOR EACH issue_location IN identified_locations:
    BUILD context_prompt(location, surrounding_code, imports, suggestions)

    // Generate precise patch variant
    precise_patch = precise_llm.INVOKE(context_prompt)
    EXTRACT code_block FROM precise_patch.response
    STORE precise_patch[location_id] = extracted_code

    // Generate diverse patch variants
    diverse_patches = []
    FOR variant_num = 1 TO 8:
        variant_response = creative_llm.INVOKE(context_prompt)
        variant_code = EXTRACT_CODE(variant_response)
        diverse_patches.APPEND(variant_code)
    END FOR
    STORE variant_patches[location_id] = diverse_patches
END FOR

## Phase 4: Atomic Multi-File Patch Application
FUNCTION apply_patches_and_generate_diff(patch_collection):
    file_modifications = CREATE_EMPTY_MAP()
    // Group patches by target files
    FOR EACH location, patch_code IN patch_collection:
        target_file = GET_FILE_PATH(location)
        line_range = GET_LINE_RANGE(location)
        file_modifications[target_file].ADD(line_range, patch_code)
    END FOR

    // Apply modifications atomically (reverse order)
    FOR EACH file IN file_modifications:
        original_content = READ_FILE(file)
        modifications = SORT_REVERSE_BY_LINE_NUMBER(file_modifications[file])
        FOR EACH modification IN modifications:
            REPLACE_LINES(original_content, modification.range, modification.code)
        END FOR
        WRITE_FILE(file, modified_content)
    END FOR

    diff_output = EXECUTE_GIT_DIFF(repository_root)
    RESTORE_ORIGINAL_FILES(original_state)
    RETURN diff_output
END FUNCTION

## Phase 5: Comprehensive Results Export
all_patch_variants = INITIALIZE_COLLECTION()
all_patch_variants["precise_patches"] = precise_patches
FOR variant_index = 1 TO 8:
    variant_set = EXTRACT_VARIANT(diverse_patches, variant_index)
    variant_diff = apply_patches_and_generate_diff(variant_set)
    all_patch_variants[f"variant_{variant_index}"] = variant_diff
END FOR

final_results = {
    "patch_variants": all_patch_variants,
    "git_diffs": diff_collection,
    "metadata": execution_statistics
}
EXPORT_JSON(final_results, output_directory)

# LLM System Configuration
PRIMARY_MODEL = ADVANCED_LLM_BACKEND
TEMPERATURE_PRECISE = 0.0 // Deterministic responses
TEMPERATURE_CREATIVE = 0.8 // Diverse solution generation
CONTEXT_THRESHOLD = 16 // Message count for summarization trigger
TOKEN_OPTIMIZATION = ENABLED // Intelligent content compression

# Performance Monitoring System
ENABLE api_statistics_collection()
TRACK prompt_content, response_content
MONITOR token_usage(prompt_tokens, completion_tokens, total_tokens)
LOG execution_timestamps
EXPORT performance_metrics TO json_format
IMPLEMENT real_time_analytics_dashboard()

# Multi-Agent State Management Schema

DEFINE AgentState EXTENDS MessagesState:
    // Core workflow state
    conversation_history: MessageSequence
    issue_locations: List[LocationDescriptor]
    repair_suggestions: StrategicAnalysis
    generated_patches: PatchCollection

    // Agent coordination flags
    locator_ready: Boolean
    suggester_ready: Boolean
    fixer_ready: Boolean

    // Context management
    conversation_summary: CompressedContext
    current_agent: AgentIdentifier
    next_agent: AgentIdentifier
    execution_metrics: PerformanceCounters

    // Problem context
    problem_statement: ProblemDescription
    project_context: ProjectMetadata
    failed_attempts: List[FailureRecord]
END DEFINE