Analytics Engine

Unlock powerful insights from your testing data with advanced analytics, trend analysis, and AI-powered recommendations.

bilgi

Coming Soon This feature is currently in development.

Analytics Overview

Key Metrics

Category	Metrics
Quality	Pass rate, defect density, coverage
Velocity	Tests executed, cycle time
Efficiency	Automation rate, reuse ratio
Health	Flaky tests, aging defects

Data Sources

All RabbitQA modules contribute data:

Analytics Data Flow
├── TestPilot → Test executions, results
├── CaseWriter → Test cases, coverage
├── AutoRunner → Automation metrics
├── Analyzer → Analysis scores
├── SmartAPI → API test results
├── Accessibility → Compliance data
└── All → Cross-module insights

Quality Analytics

Pass Rate Analysis

Track test success over time:

Metric	Description
Overall Pass Rate	All tests combined
By Module	Per application area
By Type	Manual vs automated
By Environment	Dev, staging, prod

Defect Analytics

Analysis	Insight
Defect Density	Defects per test case
Severity Distribution	Critical vs minor
Aging Analysis	Time to resolution
Root Cause	Common failure patterns

Coverage Analytics

Metric	Measurement
Requirement Coverage	Tests per requirement
Code Coverage	Lines/branches tested
Feature Coverage	Features with tests
Risk Coverage	High-risk areas tested

Trend Analysis

Time-Based Trends

Period	Use Case
Daily	Immediate issues
Weekly	Sprint progress
Monthly	Long-term trends
Release	Version comparison

Trend Visualization

Pass Rate Trend (Last 6 Months)
100% ┤                              ▄▄▄▄
 95% ┤                    ▄▄▄▄▄▄▄▄▄▄████
 90% ┤          ▄▄▄▄▄▄▄▄▄▄███████████████
 85% ┤ ▄▄▄▄▄▄▄▄████████████████████████████
 80% ┼──────────────────────────────────────
     │  Jul   Aug   Sep   Oct   Nov   Dec

Comparison Analysis

Comparison	Insight
Period vs Period	Week over week
Release vs Release	Version quality
Team vs Team	Performance comparison
Planned vs Actual	Goal tracking

Advanced Analytics

Correlation Analysis

Discover relationships:

• Test failures ↔ Code changes
• Defect rate ↔ Team velocity
• Coverage ↔ Quality
• Automation ↔ Efficiency

Predictive Analytics

AI-powered predictions:

Prediction	Use Case
Quality Forecast	Expected pass rate
Risk Prediction	High-risk areas
Effort Estimation	Testing time needed
Defect Prediction	Expected defect count

Anomaly Detection

Automatic detection of:

• Unusual failure patterns
• Performance degradation
• Coverage gaps
• Process deviations

Custom Metrics

Creating Metrics

Define custom calculations:

Metric:
  name: Test Efficiency Score
  formula: |
    (passed_tests / total_tests) *
    (automated_tests / total_tests) *
    (1 - flaky_rate)

  dimensions:
    - project
    - sprint
    - team

  thresholds:
    good: '> 0.8'
    warning: '0.6 - 0.8'
    critical: '< 0.6'

Calculated Fields

Calculation	Example
Percentage	pass_rate = passed / total
Ratio	automation_ratio = auto / manual
Average	avg_duration = sum(duration) / count
Delta	change = current - previous

Drill-Down Analysis

Hierarchical Navigation

Organization
└── Project: E-commerce
    └── Module: Checkout
        └── Feature: Payment
            └── Test Case: Credit Card
                └── Execution #1234

Filter & Segment

Dimension	Values
Time	Date range
Project	All or selected
Module	Application areas
Type	Manual, automated
Status	Passed, failed, skipped
Priority	Critical, high, medium, low

AI Insights

Automated Insights

Reporter generates insights automatically:

Insight Type	Example
Trend Alert	"Pass rate dropped 5% this week"
Anomaly	"Unusual failure spike on Tuesday"
Correlation	"Failures correlate with module X changes"
Recommendation	"Consider adding tests for Y"

Natural Language Queries

Ask questions in plain English:

• "What is the pass rate for the checkout module?"
• "Show me failed tests from last sprint"
• "Which tests are most flaky?"
• "Compare this release to the last one"

Export & Integration

Data Export

Format	Use Case
CSV	Spreadsheet analysis
JSON	API consumption
SQL	Database queries
API	Real-time access

BI Integration

Connect to external BI tools:

• Power BI
• Tableau
• Looker
• Custom dashboards

Best Practices

Analysis

• Focus on actionable metrics
• Establish baselines
• Track trends, not just snapshots
• Correlate with business outcomes

Reporting

• Share insights, not just data
• Visualize appropriately
• Provide context
• Include recommendations