ACCELQ Dashboard Widgets: Practical Guide for QA Insights and Decision Making

Introduction

This guide provides practical examples and use cases for building powerful dashboard widgets in ACCELQ that deliver actionable QA insights and support data-driven decision making. Unlike generic capability overviews, this focuses on real-world scenarios that QA practitioners face daily.

Understanding the Three Core Dimensions

ACCELQ organizes dashboard data into three primary dimensions:

1. Test Assets - Your test inventory (Scenarios, Test Cases, Actions, Contexts)
2. Test Execution - Runtime results and performance metrics
3. User Activity - Team productivity and collaboration insights

Each dimension offers unique filtering and grouping possibilities to create targeted visualizations for specific stakeholder needs.

Strategic Widget Categories for QA Teams

1. Quality Monitoring Widgets

Test Execution Status Overview

• Purpose: Real-time visibility into test suite health
• Configuration:
  • Entity: Test Executions
  • Filter: Last 7 days, specific environments
  • Group by: App Environment → Test Case Execution Status
  • Chart: Bar chart
• Business Value: Immediate identification of quality issues across environments
• Stakeholder: QA Managers, Development Teams

Pass Rate Trends

• Purpose: Track quality trajectory over time
• Configuration:
  • Entity: Test Executions
  • Filter: Given Duration (last 30 days)
  • Group by: Test Case Execution Date → Test Case Execution Status
  • Chart: Line graph
• Business Value: Identify quality regression patterns and improvement trends
• Stakeholder: QA Managers, Product Owners

2. Performance & Efficiency Widgets

Execution Time Analysis

• Purpose: Identify slow-running tests impacting CI/CD pipeline
• Configuration:
  • Entity: Test Executions
  • Filter: CI/CD Jobs, Last 'n' instances (50)
  • Group by: Average Execution Time → Test Case Execution Status
  • Chart: Bar chart
• Business Value: Optimize test suite performance for faster feedback
• Stakeholder: DevOps Teams, QA Engineers

Test Stability Monitoring

• Purpose: Identify flaky tests requiring attention
• Configuration:
  • Entity: Test Executions
  • Filter: Test Stability Score (Low/Medium thresholds)
  • Group by: Executed by User → Test Stability Score
  • Chart: Pie chart
• Business Value: Prioritize test maintenance efforts
• Stakeholder: QA Engineers, Test Automation Teams

3. Coverage & Compliance Widgets

Requirement Traceability Coverage

• Purpose: Ensure comprehensive test coverage
• Configuration:
  • Entity: Test Case
  • Filter: Has Requirement Traceability
  • Group by: Requirement Association → Status
  • Chart: Pie chart
• Business Value: Identify gaps in requirement coverage
• Stakeholder: QA Managers, Compliance Teams

Defect Mapping Status

• Purpose: Track defect resolution coverage
• Configuration:
  • Entity: Test Case
  • Filter: Is Defect Mapped
  • Group by: Is Defect Mapped → Created User
  • Chart: Bar chart
• Business Value: Ensure all defects have associated test coverage
• Stakeholder: QA Managers, Development Teams

4. Team Productivity Widgets

Test Asset Creation Velocity

• Purpose: Monitor team productivity and workload distribution
• Configuration:
  • Entity: Test Case
  • Filter: Created Date (last 30 days)
  • Group by: Created User → Created Date
  • Chart: Bar chart
• Business Value: Balance workload and identify productivity trends
• Stakeholder: QA Managers, Team Leads

User Activity Heatmap

• Purpose: Understand team collaboration patterns
• Configuration:
  • Entity: User Activity
  • Filter: Activity Date (last 14 days)
  • Group by: User → Activity Type
  • Chart: Table view
• Business Value: Identify active contributors and collaboration bottlenecks
• Stakeholder: QA Managers, Project Managers

5. Environment-Specific Widgets

Cross-Browser Test Results

• Purpose: Ensure consistent quality across browsers
• Configuration:
  • Entity: Test Executions
  • Filter: Web Browser, Last 'n' instances (100)
  • Group by: Web Browser → Web OS → Test Case Execution Status
  • Chart: Bar chart
• Business Value: Identify browser-specific issues quickly
• Stakeholder: QA Engineers, Frontend Teams

Mobile Platform Coverage

• Purpose: Track mobile testing effectiveness
• Configuration:
  • Entity: Test Executions
  • Filter: Mobile OS, Given Duration (last 7 days)
  • Group by: Mobile OS → App Environment
  • Chart: Pie chart
• Business Value: Ensure balanced mobile platform testing
• Stakeholder: Mobile QA Teams, Product Owners

6. Custom Field Intelligence Widgets

Priority-Based Test Distribution

• Purpose: Ensure high-priority features get adequate testing
• Configuration:
  • Entity: Test Case
  • Filter: Custom Fields (Priority: High, Medium, Low)
  • Group by: Custom Fields (Priority) → Status
  • Chart: Pie chart
• Business Value: Validate test effort allocation matches business priorities
• Stakeholder: QA Managers, Product Owners

Feature Team Execution Status

• Purpose: Track quality metrics by feature team
• Configuration:
  • Entity: Test Executions
  • Filter: Custom Fields (Feature Team)
  • Group by: Custom Fields (Feature Team) → Test Case Execution Status
  • Chart: Bar chart
• Business Value: Identify teams needing quality support
• Stakeholder: Engineering Managers, QA Managers

Advanced Widget Configurations

Multi-Level Grouping for Deep Insights

Execution Efficiency by Environment and Browser

• Purpose: Identify performance bottlenecks across configurations
• Configuration:
  • Entity: Test Executions
  • Primary Group: App Environment
  • Secondary Group: Web Browser
  • Tertiary Group: Execution Efficiency
  • Chart: Bar chart with drill-down capability
• Business Value: Pinpoint specific environment-browser combinations causing issues

Time-Based Analysis Widgets

Weekly Quality Trends

• Purpose: Track quality patterns across different time periods
• Configuration:
  • Entity: Test Executions
  • Filter: Given Duration (last 12 weeks)
  • Group by: Test Case Execution Date (weekly) → Test Case Execution Status
  • Chart: Line graph
• Business Value: Identify cyclical quality patterns and sprint impact

Comparative Analysis Widgets

Automation vs Manual Test Results

• Purpose: Compare effectiveness of automated vs manual testing
• Configuration:
  • Entity: Test Executions
  • Filter: Asset Type (Automation vs Manual)
  • Group by: Asset Type → Test Case Execution Status
  • Chart: Side-by-side bar chart
• Business Value: Justify automation investment and identify coverage gaps

Common Use Case Scenarios

Release Readiness Assessment

Widget Combination:

1. Overall pass rate for release candidate
2. Critical path test status
3. Environment-specific results
4. Known defect impact analysis

Decision Support: Go/No-go decision based on comprehensive quality metrics

Sprint Quality Review

Widget Combination:

1. New test case creation velocity
2. Execution results for sprint features
3. Team productivity metrics
4. Defect resolution coverage

Decision Support: Sprint retrospective insights and next sprint planning

Test Maintenance Planning

Widget Combination:

1. Flaky test identification
2. Execution time analysis
3. Test stability trends
4. Asset modification frequency

Decision Support: Prioritize test refactoring and maintenance efforts

Advanced Analytics Features

Execution Analytics Metrics

ACCELQ provides advanced analytical capabilities for execution data with configurable thresholds:

Flakiness Score

• Definition: Rate of change of pass/fail status across executions
• Calculation: Measures test result consistency over time
• Thresholds: High/Medium/Low (configurable)
• Use Case: Identify unreliable tests requiring stabilization

Pass Percentage

• Definition: Ratio of successful executions to total executions
• Calculation: (Passed executions / Total executions) × 100
• Thresholds: High/Medium/Low (configurable)
• Use Case: Track test reliability and quality trends

Average Execution Time

• Definition: Mean duration of test execution across runs
• Calculation: Sum of all execution times / Number of executions
• Thresholds: High/Medium/Low (configurable)
• Use Case: Identify performance bottlenecks in test suite

Execution Efficiency Score

• Definition: Ratio of successful executions to total execution time
• Calculation: (Number of passed executions / Total execution time)
• Thresholds: High/Medium/Low (configurable)
• Use Case: Optimize test suite for maximum value per time invested

These analytics provide data-driven insights for test optimization, helping teams focus their efforts on the most impactful improvements to their test suites.

Conclusion

Effective dashboard widgets transform raw test data into actionable insights that drive quality decisions. By focusing on specific use cases, stakeholder needs, and business outcomes, QA teams can create powerful visualization tools that not only monitor current quality but also predict and prevent future issues.

The key to success lies in starting with clear objectives, choosing appropriate visualizations, and continuously refining widgets based on user feedback and changing business needs. Remember that the most valuable widgets are those that directly support decision-making and drive meaningful improvements in your QA process.