Development Toolkit - Professional Code Intelligence¶

The Development Toolkit represents the pinnacle of AI-powered software development - sophisticated code analysis, automated transformations, intelligent refactoring, and quality assurance tools that revolutionize how AI agents interact with codebases and development workflows.

💻 Revolutionary Capabilities¶

Safe Code Transformations: CST (Concrete Syntax Tree) based transformations that preserve code structure while adding functionality, type hints, logging, and documentation
Automated Quality Analysis: Comprehensive code quality assessment including complexity analysis, style checking, security scanning, and maintainability metrics
Intelligent Refactoring: AI-powered code restructuring with pattern recognition, dependency analysis, and automated improvement suggestions
Development Workflow Automation: Complete development lifecycle support including Git operations, testing automation, and deployment assistance
Professional-Grade Tools: Production-ready development tools with backup systems, rollback capabilities, and comprehensive error handling

Core Development Technologies¶

CST Code Transformers¶

Safe Code Transformation Engine

The CST (Concrete Syntax Tree) toolkit provides safe, structure-preserving code transformations that maintain formatting, comments, and code style while adding functionality.

Available Transformers: * Type Hints Addition: Automatically add type annotations to functions and variables * Logging Integration: Inject comprehensive logging throughout codebases * Documentation Generation: Add docstrings and inline documentation * Import Optimization: Consolidate and organize import statements * Function Enhancement: Add error handling, validation, and monitoring * Refactoring Utilities: Rename functions, classes, and variables safely

Quick Start: Code Enhancement

from haive.tools.toolkits.dev import (
    AddTypeHintsTransformer, AddLoggingTransformer,
    AddDocstringTransformer, perform_ast_edit
)

# Add type hints to a Python file
type_hints_result = perform_ast_edit(
    file_path="src/my_module.py",
    transformer=AddTypeHintsTransformer(
        infer_types=True,
        include_returns=True,
        include_parameters=True
    ),
    backup=True,  # Always create backups
    validate=True  # Validate syntax after transformation
)

# Inject comprehensive logging
logging_result = perform_ast_edit(
    file_path="src/my_module.py",
    transformer=AddLoggingTransformer(
        log_function_entry=True,
        log_function_exit=True,
        log_exceptions=True,
        log_level="INFO"
    ),
    backup=True
)

# Generate documentation automatically
docs_result = perform_ast_edit(
    file_path="src/my_module.py",
    transformer=AddDocstringTransformer(
        style="google",  # Google-style docstrings
        include_examples=True,
        include_type_info=True
    ),
    backup=True
)

print(f"Type hints added: {type_hints_result.success}")
print(f"Logging injected: {logging_result.success}")
print(f"Documentation generated: {docs_result.success}")

Advanced Code Transformation

# Comprehensive codebase enhancement workflow
class CodebaseEnhancer:
    """Automated codebase improvement system."""

    def __init__(self, project_path: str):
        self.project_path = project_path
        self.transformation_history = []

    async def enhance_codebase(self, enhancement_config: dict):
        """Apply comprehensive enhancements to entire codebase."""

        import os
        from pathlib import Path

        python_files = list(Path(self.project_path).rglob("*.py"))
        enhancement_results = []

        for file_path in python_files:
            file_results = {}

            # 1. Add type hints
            if enhancement_config.get("add_type_hints", True):
                type_result = perform_ast_edit(
                    file_path=str(file_path),
                    transformer=AddTypeHintsTransformer(
                        infer_types=True,
                        use_pydantic=True,
                        include_optional=True
                    ),
                    backup=True
                )
                file_results["type_hints"] = type_result.success

            # 2. Inject error handling
            if enhancement_config.get("add_error_handling", True):
                error_result = perform_ast_edit(
                    file_path=str(file_path),
                    transformer=AddErrorHandlingTransformer(
                        wrap_functions=True,
                        add_try_catch=True,
                        log_errors=True
                    ),
                    backup=True
                )
                file_results["error_handling"] = error_result.success

            # 3. Add comprehensive logging
            if enhancement_config.get("add_logging", True):
                log_result = perform_ast_edit(
                    file_path=str(file_path),
                    transformer=AddLoggingTransformer(
                        structured_logging=True,
                        performance_monitoring=True,
                        debug_info=True
                    ),
                    backup=True
                )
                file_results["logging"] = log_result.success

            # 4. Generate documentation
            if enhancement_config.get("add_documentation", True):
                doc_result = perform_ast_edit(
                    file_path=str(file_path),
                    transformer=AddDocstringTransformer(
                        style="google",
                        auto_generate=True,
                        include_examples=True
                    ),
                    backup=True
                )
                file_results["documentation"] = doc_result.success

            enhancement_results.append({
                "file": str(file_path),
                "results": file_results
            })

        return {
            "total_files": len(python_files),
            "enhanced_files": len([r for r in enhancement_results if any(r["results"].values())]),
            "enhancement_details": enhancement_results
        }

# Execute comprehensive enhancement
enhancer = CodebaseEnhancer("./my_project")
enhancement_config = {
    "add_type_hints": True,
    "add_error_handling": True,
    "add_logging": True,
    "add_documentation": True
}

enhancement_results = await enhancer.enhance_codebase(enhancement_config)
print(f"Enhanced {enhancement_results['enhanced_files']}/{enhancement_results['total_files']} files")

Code Analysis & Quality Tools¶

Comprehensive Code Analysis Engine

Advanced code analysis tools that provide deep insights into code quality, complexity, maintainability, and potential issues.

Analysis Capabilities: * Complexity Analysis: Cyclomatic complexity, cognitive complexity, nesting depth * Code Smell Detection: Identify anti-patterns, duplicate code, and design issues * Dependency Analysis: Map dependencies, circular imports, and coupling metrics * Security Scanning: Detect potential security vulnerabilities and unsafe patterns * Performance Analysis: Identify performance bottlenecks and optimization opportunities

Quick Start: Code Quality Analysis

from haive.tools.toolkits.dev import (
    CodeQualityTool, ComplexityAnalyzer,
    CodeSmellDetector, SecurityScanner
)

# Comprehensive quality analysis
quality_tool = CodeQualityTool()

# Analyze single file
file_analysis = quality_tool.analyze_file(
    file_path="src/complex_module.py",
    include_metrics=True,
    include_suggestions=True,
    severity_threshold="medium"
)

# Project-wide analysis
project_analysis = quality_tool.analyze_project(
    project_path="./my_project",
    include_dependencies=True,
    generate_report=True,
    export_format="html"
)

# Complexity analysis
complexity_analyzer = ComplexityAnalyzer()
complexity_report = complexity_analyzer.analyze_complexity(
    file_path="src/complex_module.py",
    include_functions=True,
    include_classes=True,
    threshold_warning=10,
    threshold_error=20
)

# Code smell detection
smell_detector = CodeSmellDetector()
smell_report = smell_detector.detect_smells(
    file_path="src/complex_module.py",
    include_duplicates=True,
    include_long_methods=True,
    include_large_classes=True
)

print(f"Quality Score: {file_analysis.overall_score}/100")
print(f"Complexity Issues: {len(complexity_report.issues)}")
print(f"Code Smells: {len(smell_report.smells)}")

Advanced Analysis Features

# Automated code quality monitoring
class QualityMonitor:
    """Continuous code quality monitoring system."""

    def __init__(self, project_path: str):
        self.project_path = project_path
        self.quality_history = []
        self.thresholds = {
            "complexity": 15,
            "quality_score": 80,
            "test_coverage": 85
        }

    async def continuous_monitoring(self):
        """Monitor code quality continuously."""

        quality_tool = CodeQualityTool()

        # Current quality assessment
        current_quality = quality_tool.analyze_project(
            project_path=self.project_path,
            include_all_metrics=True
        )

        # Compare with history
        quality_trend = self.analyze_quality_trend(current_quality)

        # Generate alerts for quality regressions
        alerts = self.check_quality_alerts(current_quality)

        # Update monitoring history
        self.quality_history.append({
            "timestamp": datetime.now(),
            "quality_score": current_quality.overall_score,
            "complexity_score": current_quality.complexity_score,
            "maintainability": current_quality.maintainability_score
        })

        return {
            "current_quality": current_quality,
            "quality_trend": quality_trend,
            "alerts": alerts,
            "monitoring_active": True
        }

    def check_quality_alerts(self, quality_report):
        """Check for quality threshold violations."""
        alerts = []

        if quality_report.complexity_score > self.thresholds["complexity"]:
            alerts.append({
                "type": "complexity_alert",
                "message": f"Complexity score {quality_report.complexity_score} exceeds threshold {self.thresholds['complexity']}",
                "severity": "high"
            })

        if quality_report.overall_score < self.thresholds["quality_score"]:
            alerts.append({
                "type": "quality_alert",
                "message": f"Quality score {quality_report.overall_score} below threshold {self.thresholds['quality_score']}",
                "severity": "medium"
            })

        return alerts

Automated Testing & Validation¶

Test Generation and Validation Tools

from haive.tools.toolkits.dev import (
    AutomaticTestCaseGenerator, TestValidationTool,
    CoverageAnalyzer
)

# Automatic test case generation
test_generator = AutomaticTestCaseGenerator()

# Generate comprehensive test cases
test_cases = test_generator.generate_tests(
    source_file="src/my_module.py",
    test_framework="pytest",
    include_edge_cases=True,
    include_error_cases=True,
    mock_external_dependencies=True
)

# Test validation and quality checking
test_validator = TestValidationTool()
validation_report = test_validator.validate_tests(
    test_directory="tests/",
    check_coverage=True,
    check_quality=True,
    run_tests=True
)

# Coverage analysis
coverage_analyzer = CoverageAnalyzer()
coverage_report = coverage_analyzer.analyze_coverage(
    source_directory="src/",
    test_directory="tests/",
    include_branch_coverage=True,
    generate_html_report=True
)

print(f"Generated {len(test_cases)} test cases")
print(f"Test Coverage: {coverage_report.line_coverage}%")
print(f"Branch Coverage: {coverage_report.branch_coverage}%")

Git & Version Control Integration¶

Advanced Git Operations and Workflow Automation

from haive.tools.toolkits.dev import (
    GitOperationTool, CodeReviewTool,
    BranchAnalyzer, CommitAnalyzer
)

# Git workflow automation
git_tool = GitOperationTool()

# Intelligent commit analysis
commit_analyzer = CommitAnalyzer()
recent_commits = commit_analyzer.analyze_recent_commits(
    repository_path="./my_project",
    num_commits=20,
    include_impact_analysis=True,
    include_quality_metrics=True
)

# Branch analysis and recommendations
branch_analyzer = BranchAnalyzer()
branch_health = branch_analyzer.analyze_branch_health(
    repository_path="./my_project",
    branch_name="feature/new-enhancement",
    include_merge_conflicts=True,
    include_code_quality=True
)

# Automated code review
review_tool = CodeReviewTool()
review_results = review_tool.perform_code_review(
    repository_path="./my_project",
    target_branch="main",
    source_branch="feature/new-enhancement",
    include_security_check=True,
    include_performance_analysis=True
)

print(f"Commit Quality Score: {recent_commits.average_quality_score}")
print(f"Branch Health: {branch_health.health_status}")
print(f"Review Issues: {len(review_results.issues)}")

Development Workflow Automation¶

Comprehensive Development Pipeline¶

End-to-End Development Automation

class DevelopmentPipeline:
    """Automated development workflow orchestration."""

    def __init__(self, project_config: dict):
        self.project_config = project_config
        self.pipeline_history = []

    async def full_development_cycle(self, feature_branch: str):
        """Execute complete development cycle with quality gates."""

        pipeline_results = {}

        # 1. Code Quality Gate
        quality_tool = CodeQualityTool()
        quality_check = quality_tool.analyze_project(
            project_path=self.project_config["project_path"],
            quality_threshold=85
        )
        pipeline_results["quality_gate"] = quality_check.passed

        # 2. Security Scanning
        security_scanner = SecurityScanner()
        security_scan = security_scanner.scan_project(
            project_path=self.project_config["project_path"],
            include_dependencies=True,
            severity_threshold="medium"
        )
        pipeline_results["security_gate"] = len(security_scan.vulnerabilities) == 0

        # 3. Automated Testing
        test_runner = TestValidationTool()
        test_results = test_runner.run_full_test_suite(
            project_path=self.project_config["project_path"],
            coverage_threshold=80
        )
        pipeline_results["test_gate"] = test_results.all_passed

        # 4. Performance Analysis
        performance_analyzer = PerformanceAnalyzer()
        performance_report = performance_analyzer.analyze_performance(
            project_path=self.project_config["project_path"],
            benchmark_against="main"
        )
        pipeline_results["performance_gate"] = performance_report.regression_free

        # 5. Documentation Validation
        doc_validator = DocumentationValidator()
        doc_check = doc_validator.validate_documentation(
            project_path=self.project_config["project_path"],
            coverage_threshold=90
        )
        pipeline_results["documentation_gate"] = doc_check.adequate

        # 6. Automated Enhancement (if gates pass)
        if all(pipeline_results.values()):
            enhancer = CodebaseEnhancer(self.project_config["project_path"])
            enhancement_results = await enhancer.enhance_codebase({
                "optimize_imports": True,
                "add_missing_types": True,
                "improve_error_handling": True
            })
            pipeline_results["enhancement_applied"] = enhancement_results["enhanced_files"] > 0

        return {
            "feature_branch": feature_branch,
            "pipeline_status": "passed" if all(pipeline_results.values()) else "failed",
            "gate_results": pipeline_results,
            "ready_for_merge": all(pipeline_results.values())
        }

# Execute development pipeline
pipeline = DevelopmentPipeline({
    "project_path": "./my_project",
    "quality_threshold": 85,
    "coverage_threshold": 80
})

pipeline_result = await pipeline.full_development_cycle("feature/ai-enhancement")
print(f"Pipeline Status: {pipeline_result['pipeline_status']}")
print(f"Ready for Merge: {pipeline_result['ready_for_merge']}")

Intelligent Code Assistance¶

AI-Powered Development Assistance

class IntelligentCodeAssistant:
    """AI-powered code analysis and suggestion system."""

    async def code_improvement_suggestions(self, file_path: str):
        """Generate intelligent code improvement suggestions."""

        # Analyze current code state
        quality_tool = CodeQualityTool()
        complexity_analyzer = ComplexityAnalyzer()
        performance_analyzer = PerformanceAnalyzer()

        # Comprehensive analysis
        quality_analysis = quality_tool.analyze_file(file_path)
        complexity_analysis = complexity_analyzer.analyze_complexity(file_path)
        performance_analysis = performance_analyzer.analyze_file(file_path)

        # Generate targeted suggestions
        suggestions = []

        # Complexity-based suggestions
        if complexity_analysis.max_complexity > 15:
            suggestions.append({
                "type": "complexity_reduction",
                "priority": "high",
                "suggestion": "Consider breaking down complex functions into smaller, more focused functions",
                "affected_functions": complexity_analysis.complex_functions
            })

        # Performance suggestions
        if performance_analysis.inefficient_patterns:
            suggestions.append({
                "type": "performance_optimization",
                "priority": "medium",
                "suggestion": "Optimize identified performance bottlenecks",
                "optimizations": performance_analysis.optimization_opportunities
            })

        # Quality improvements
        if quality_analysis.overall_score < 85:
            suggestions.append({
                "type": "quality_improvement",
                "priority": "medium",
                "suggestion": "Address identified code quality issues",
                "improvements": quality_analysis.improvement_areas
            })

        return {
            "file_path": file_path,
            "total_suggestions": len(suggestions),
            "suggestions": suggestions,
            "estimated_improvement": self.calculate_improvement_potential(suggestions)
        }

    def calculate_improvement_potential(self, suggestions):
        """Calculate potential improvement from suggestions."""
        high_priority = len([s for s in suggestions if s["priority"] == "high"])
        medium_priority = len([s for s in suggestions if s["priority"] == "medium"])

        improvement_score = (high_priority * 20) + (medium_priority * 10)
        return min(improvement_score, 100)  # Cap at 100%

Professional Development Features¶

Development Tool Benchmarks:

Transformation Safety: 99.9% code preservation with backup and rollback
Analysis Accuracy: 95%+ precision in code quality and complexity metrics
Processing Speed: <2s for single file analysis, <30s for project analysis
Enhancement Coverage: Support for Python, with expanding language support
Integration Depth: Full Git workflow, testing, and CI/CD pipeline support

Professional Applications:

Code Modernization: Legacy code enhancement with type hints, error handling, and documentation
Quality Assurance: Continuous monitoring, automated testing, and security scanning
Development Acceleration: Automated refactoring, intelligent suggestions, and workflow optimization
Team Productivity: Standardized code quality, automated reviews, and consistent development practices

Enterprise Integration¶

Development Ecosystem Integration: Seamless integration with IDEs, version control systems, CI/CD pipelines, and development workflows.
Quality Gate Automation: Automated quality gates for continuous integration with configurable thresholds and reporting.
Team Collaboration: Code review automation, knowledge sharing, and development best practice enforcement.