AI-Powered Testing: Machine Learning for Bug Detection 2026

# Example: Using AI to prioritize code review
import subprocess
from collections import defaultdict

class AIPriorityReviewer:
    def __init__(self, model_path):
        self.model = self.load_model(model_path)
        
    def analyze_changes(self, diff):
        """Analyze code changes and predict bug likelihood."""
        features = self.extract_features(diff)
        risk_score = self.model.predict_risk(features)
        
        return {
            'risk_score': risk_score,
            'priority': 'high' if risk_score > 0.7 else 'medium' if risk_score > 0.4 else 'low',
            'predicted_issues': self.model.predict_issues(features),
            'review_focus_areas': self.model.suggest_focus(features)
        }
    
    def prioritize_tests(self, changed_files):
        """Recommend which tests to run based on risk."""
        test_impact = defaultdict(float)
        
        for file in changed_files:
            risk = self.analyze_file(file)
            affected_tests = self.find_affected_tests(file)
            
            for test in affected_tests:
                test_impact[test] += risk * self.coverage_weight(test)
        
        return sorted(test_impact.items(), key=lambda x: x[1], reverse=True)

# Example: LLM-powered test generation
class LLMTestGenerator:
    def __init__(self, llm_client):
        self.llm = llm_client
        
    def generate_tests(self, function_code, function_signature):
        """Generate test cases using LLM."""
        prompt = f"""
        Generate comprehensive test cases for this function:
        
        Function Signature: {function_signature}
        Implementation: {function_code}
        
        Include:
        1. Happy path tests
        2. Edge cases
        3. Error handling tests
        4. Boundary value tests
        
        Write pytest-compatible test code.
        """
        
        response = self.llm.complete(prompt)
        return self.parse_test_cases(response)
    
    def generate_property_tests(self, code):
        """Generate property-based tests using AI."""
        # Analyze function to identify invariants
        invariants = self.identify_invariants(code)
        
        # Generate hypothesis tests
        tests = []
        for invariant in invariants:
            test = self.create_property_test(invariant)
            tests.append(test)
        
        return tests

class AIMutationTester:
    def __init__(self):
        self.analysis_model = load_mutation_impact_model()
        
    def select_mutations(self, source_code, time_budget):
        """Use AI to select most valuable mutations."""
        mutations = self.get_all_possible_mutations(source_code)
        
        scored_mutations = []
        for mutation in mutations:
            impact = self.analysis_model.predict_impact(source_code, mutation)
            difficulty = self.estimate_detection_difficulty(mutation)
            score = impact * difficulty
            scored_mutations.append((mutation, score))
        
        # Select mutations within time budget
        selected = []
        total_time = 0
        for mutation, score in sorted(scored_mutations, key=lambda x: x[1], reverse=True):
            estimated_time = self.estimate_time(mutation)
            if total_time + estimated_time <= time_budget:
                selected.append(mutation)
                total_time += estimated_time
                
        return selected

class AIVisualTester:
    def __init__(self):
        self.diff_model = load_visual_diff_model()
        self.layout_model = load_layout_model()
        
    def compare_screenshots(self, baseline, current, threshold=0.1):
        """Use AI to compare screenshots intelligently."""
        # Traditional pixel diff
        pixel_diff = self.pixel_diff(baseline, current)
        
        # AI-powered semantic diff
        semantic_changes = self.diff_model.detect_changes(baseline, current)
        
        # Filter out trivial changes
        significant_changes = [
            change for change in semantic_changes
            if change.confidence > threshold
        ]
        
        return {
            'pixel_diff_ratio': pixel_diff,
            'significant_changes': significant_changes,
            'layout_shifts': self.layout_model.detect_shifts(baseline, current),
            'text_changes': self.extract_text_changes(semantic_changes)
        }
    
    def detect_layout_shifts(self, before, after):
        """Detect and categorize layout shifts."""
        shifts = self.layout_model.identify_shifts(before, after)
        
        categorized = {
            'major': [],
            'minor': [],
            'negligible': []
        }
        
        for shift in shifts:
            if shift.impact > 0.2:
                categorized['major'].append(shift)
            elif shift.impact > 0.05:
                categorized['minor'].append(shift)
            else:
                categorized['negligible'].append(shift)
        
        return categorized

class MLTestPrioritizer:
    def __init__(self, historical_data):
        self.model = self.build_priority_model(historical_data)
        self.coverage_analyzer = CoverageAnalyzer()
        
    def prioritize(self, changed_files, available_tests):
        """Prioritize tests based on likelihood of failure."""
        # Get code change features
        change_features = self.extract_change_features(changed_files)
        
        # Get test features
        test_features = self.extract_test_features(available_tests)
        
        # Get historical features
        history_features = self.get_historical_features(available_tests)
        
        # Combine and predict
        combined = {**change_features, **test_features, **history_features}
        failure_probability = self.model.predict(combined)
        
        # Also consider coverage
        coverage_scores = {}
        for test in available_tests:
            coverage_scores[test] = self.coverage_analyzer.compute_coverage(test, changed_files)
        
        # Final priority score
        priorities = []
        for test in available_tests:
            score = (
                failure_probability[test] * 0.5 +
                coverage_scores[test] * 0.3 +
                self.get_execution_speed_factor(test) * 0.2
            )
            priorities.append((test, score))
        
        return sorted(priorities, key=lambda x: x[1], reverse=True)

class SelfLearningTestSuite:
    def __init__(self):
        self.test_results = []
        self.improvement_model = ImprovementModel()
        
    def record_result(self, test_name, execution_time, passed, coverage):
        """Record test execution for learning."""
        self.test_results.append({
            'test': test_name,
            'time': execution_time,
            'passed': passed,
            'coverage': coverage,
            'timestamp': datetime.now()
        })
    
    def optimize(self):
        """Analyze results and suggest improvements."""
        # Find flaky tests
        flaky = self.detect_flaky_tests()
        
        # Find slow tests
        slow = self.detect_slow_tests()
        
        # Find low-value tests
        low_value = self.detect_low_value_tests()
        
        suggestions = []
        if flaky:
            suggestions.append({
                'type': 'fix_flaky',
                'tests': flaky,
                'action': 'Review and stabilize these tests'
            })
        if slow:
            suggestions.append({
                'type': 'optimize_speed',
                'tests': slow,
                'action': 'Consider parallelization or mocking'
            })
        if low_value:
            suggestions.append({
                'type': 'remove_or_update',
                'tests': low_value,
                'action': 'These tests provide little value'
            })
        
        return suggestions