AI Test Documentation: Automated Documentation from Screenshots to Insights

from ai_docs import ScreenshotAnalyzer

class BugDocumentation:
    def __init__(self):
        self.analyzer = ScreenshotAnalyzer(
            model='gpt-4-vision',
            ocr_enabled=True
        )

    def generate_bug_report(self, screenshot_path, test_context):
        analysis = self.analyzer.analyze(
            image=screenshot_path,
            context=test_context
        )

        return {
            'summary': analysis.detected_error,
            'description': analysis.detailed_description,
            'ui_elements': analysis.identified_elements,
            'error_messages': analysis.extracted_text,
            'suggested_severity': analysis.severity_assessment,
            'reproduction_hint': analysis.likely_cause
        }

# Example usage
doc = BugDocumentation()
report = doc.generate_bug_report(
    screenshot_path='failures/checkout_error.png',
    test_context={
        'test_name': 'test_checkout_flow',
        'step': 'Payment submission',
        'expected': 'Order confirmation page'
    }
)

# AI-generated output:
# {
#   'summary': 'Payment processing failed with JavaScript error',
#   'description': 'Error banner displayed at top of checkout page...',
#   'ui_elements': ['Submit button (disabled)', 'CVV field (error state)'],
#   'error_messages': ['Payment processing failed. Please try again.'],
#   'suggested_severity': 'High',
#   'reproduction_hint': 'CVV validation failing before payment submission'
# }

const { VisualDocAI } = require('visual-doc-ai');

const visualDoc = new VisualDocAI({
  baselineDir: 'screenshots/baseline',
  diffThreshold: 0.02
});

async function documentVisualChanges(currentScreenshot, baselinePath) {
  const analysis = await visualDoc.compareAndDocument({
    baseline: baselinePath,
    current: currentScreenshot,
    pageName: 'Checkout Page'
  });

  if (analysis.hasDifferences) {
    // AI generates categorized change report
    return {
      critical: analysis.changes.filter(c => c.impact === 'high'),
      medium: analysis.changes.filter(c => c.impact === 'medium'),
      minor: analysis.changes.filter(c => c.impact === 'low'),
      report: analysis.humanReadableReport
    };
  }

  return null;
}

// Example AI output:
// {
//   critical: [{
//     element: 'Submit button',
//     change: 'Color #0066CC → #FF0000',
//     impact: 'high',
//     reason: 'Primary CTA color changed'
//   }],
//   medium: [{
//     element: 'Discount input',
//     change: 'Position shifted 15px down',
//     impact: 'medium',
//     reason: 'Layout change, possibly new element above'
//   }],
//   minor: [{
//     element: 'Product title',
//     change: 'Font size 16px → 18px',
//     impact: 'low',
//     reason: 'Typography adjustment'
//   }]
// }

from ai_docs import VideoAnalyzer

class TestVideoDocumentation:
    def __init__(self):
        self.analyzer = VideoAnalyzer(
            model='action-recognition-v3',
            ocr_enabled=True
        )

    def extract_test_steps(self, video_path, test_name):
        steps = self.analyzer.extract_steps(video_path)

        return [{
            'step_number': i + 1,
            'action': step.action,
            'element': step.target_element,
            'timestamp': step.timestamp,
            'screenshot': step.key_frame_path,
            'sensitive_masked': step.contains_sensitive_data
        } for i, step in enumerate(steps)]

    def identify_failure(self, video_path):
        failure = self.analyzer.find_failure_point(video_path)

        return {
            'timestamp': failure.timestamp,
            'description': failure.what_happened,
            'technical_details': failure.extracted_errors,
            'reproduction_steps': failure.steps_to_reproduce
        }

# AI-extracted steps example:
# [
#   {'step_number': 1, 'action': 'Navigate to login page', 'timestamp': '00:00:02'},
#   {'step_number': 2, 'action': 'Enter username: test@example.com', 'timestamp': '00:00:05'},
#   {'step_number': 3, 'action': 'Enter password', 'sensitive_masked': True, 'timestamp': '00:00:08'},
#   {'step_number': 4, 'action': 'Click "Sign In" button', 'timestamp': '00:00:11'},
#   {'step_number': 5, 'action': 'Verify redirect to dashboard', 'timestamp': '00:00:14'}
# ]

from ai_docs import InsightGenerator

class TestInsights:
    def __init__(self):
        self.generator = InsightGenerator()

    def analyze_test_history(self, results, days=30):
        insights = self.generator.find_patterns(results, days)

        return {
            'flaky_tests': insights.flaky_patterns,
            'environment_issues': insights.env_correlations,
            'time_based_failures': insights.temporal_patterns,
            'performance_trends': insights.degradation_signals,
            'recommendations': insights.actionable_suggestions
        }

# AI-generated insights example:
# {
#   'flaky_tests': [{
#     'test': 'test_user_profile_update',
#     'pattern': 'Fails 30% on Chrome, 0% on Firefox',
#     'likely_cause': 'Race condition in async JS',
#     'recommendation': 'Add explicit wait for profile save'
#   }],
#   'environment_issues': [{
#     'tests': 'checkout_* suite',
#     'pattern': '15% failure on staging, 0% on dev',
#     'likely_cause': 'Payment gateway timeout >5s',
#     'recommendation': 'Increase timeout or mock payment'
#   }],
#   'performance_trends': [{
#     'component': 'Product search',
#     'pattern': 'Response time +40% over 2 weeks',
#     'likely_cause': 'Database index degradation',
#     'recommendation': 'Review search query performance'
#   }]
# }

const { ReleaseDocGenerator } = require('ai-docs');

async function generateReleaseNotes(version, dateRange) {
  const generator = new ReleaseDocGenerator({
    testResults: './test-results/',
    gitCommits: './git-log.json',
    tickets: './jira-export.json'
  });

  return await generator.create({
    version,
    startDate: dateRange.start,
    endDate: dateRange.end,
    sections: [
      'feature_coverage',
      'bug_fixes_verified',
      'coverage_changes',
      'performance_metrics',
      'known_issues',
      'risk_assessment'
    ]
  });
}

// AI-generated release notes include:
// - New features with test coverage %
// - Bug fixes with verification status
// - Coverage delta (e.g., 87% → 89%)
// - Performance metrics from load tests
// - Known issues with workarounds
// - Risk assessment (Low/Medium/High)