# Timeliness Analysis - TRUST 5 Framework

> Module: Timeliness category deep dive with performance optimization and modern practices
> Parent: [TRUST 5 Framework](./trust5-framework.md)
> Complexity: Advanced
> Time: 10+ minutes
> Dependencies: Python 3.8+, ast

## Overview

Timeliness (10% weight) validates performance and modern practices through optimization opportunity identification, deprecated code detection, performance standards validation, and technology currency checks.

## Performance Optimization Detection

### Comprehensive Performance Analysis

```python
def analyze_performance_opportunities(
    self, file_path: str, content: str, tree: ast.AST
) -> List[CodeIssue]:
    """Identify performance optimization opportunities."""

    issues = []

    # Check for inefficient data structures
    data_structure_issues = self._check_inefficient_data_structures(tree)
    issues.extend(data_structure_issues)

    # Check for missing caching opportunities
    caching_opportunities = self._identify_caching_opportunities(tree)
    issues.extend(caching_opportunities)

    # Check for suboptimal algorithms
    algorithm_issues = self._check_algorithm_efficiency(tree)
    issues.extend(algorithm_issues)

    # Check for I/O optimization opportunities
    io_issues = self._check_io_optimization(tree)
    issues.extend(io_issues)

    return issues
```

### Caching Opportunity Detection

```python
def _identify_caching_opportunities(self, tree: ast.AST) -> List[CodeIssue]:
    """Identify functions that could benefit from caching."""

    issues = []

    for node in ast.walk(tree):
        if isinstance(node, ast.FunctionDef):
            # Check for pure functions (no side effects)
            if self._is_pure_function(node) and not self._has_decorator(node, 'lru_cache'):
                # Check if function is called frequently
                if self._is_frequently_called(node, tree):
                    issue = CodeIssue(
                        id=f"caching_opportunity_{node.lineno}",
                        category=TrustCategory.TIMELINESS,
                        severity="low",
                        issue_type="performance_issue",
                        title="Caching Opportunity",
                        description=f"Function '{node.name}' could benefit from caching",
                        file_path=file_path,
                        line_number=node.lineno,
                        column_number=1,
                        code_snippet=f"def {node.name}(...):",
                        suggested_fix=f"Consider adding @lru_cache to '{node.name}'",
                        confidence=0.6,
                        rule_violated="CACHING_OPPORTUNITY"
                    )
                    issues.append(issue)

    return issues
```

## Detection Patterns

### Common Timeliness Issues

1. **Inefficient Data Structures**: Using lists where sets/dicts would be better
2. **Missing Caching**: Repeated expensive calculations without memoization
3. **String Concatenation**: Inefficient string building in loops
4. **Global Variables**: Excessive global variable usage
5. **Deprecated APIs**: Use of deprecated functions/modules
6. **Unoptimized Loops**: Nested loops without early exit
7. **Missing Async**: Blocking I/O without async/await

### Technology Currency Checks

```python
def check_technology_currency(
    self, file_path: str, content: str
) -> List[CodeIssue]:
    """Check for outdated technology usage."""

    issues = []

    # Check for deprecated imports
    deprecated = [
        'from threading import Timer',  # Use asyncio instead
        'import time',  # Consider time.perf_counter()
    ]

    for dep in deprecated:
        if dep in content:
            issue = CodeIssue(
                id=f"deprecated_{dep}",
                category=TrustCategory.TIMELINESS,
                severity="low",
                issue_type="code_smell",
                title="Deprecated Pattern",
                description=f"Consider modern alternatives to '{dep}'",
                file_path=file_path,
                suggested_fix="Update to current best practices",
                confidence=0.7,
                rule_violated="DEPRECATED_PATTERN"
            )
            issues.append(issue)

    return issues
```

## Performance Best Practices

### Algorithmic Efficiency

```python
def _check_algorithm_efficiency(self, tree: ast.AST) -> List[CodeIssue]:
    """Check for suboptimal algorithm choices."""

    issues = []

    # Check for O(n^2) operations in loops
    for node in ast.walk(tree):
        if isinstance(node, ast.For):
            if self._has_nested_linear_search(node):
                issue = CodeIssue(
                    id=f"inefficient_search_{node.lineno}",
                    category=TrustCategory.TIMELINESS,
                    severity="medium",
                    issue_type="performance_issue",
                    title="Inefficient Search",
                    description="Linear search in nested loop",
                    file_path=file_path,
                    line_number=node.lineno,
                    suggested_fix="Use set/dict for O(1) lookup",
                    confidence=0.8,
                    rule_violated="INEFFICIENT_ALGORITHM"
                )
                issues.append(issue)

    return issues
```

## Best Practices

1. Profile First: Measure before optimizing
2. Data Structures: Choose appropriate data structures
3. Caching: Apply caching to pure functions
4. I/O Bound: Use async for I/O operations
5. Regular Updates: Stay current with best practices

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Version: 1.0.0
Last Updated: 2026-01-06
