Widgets Reference

This reference lists all widgets available in Custom Dashboards and describes what each widget displays and how to interpret it.

Each widget includes the following fields:

Widget: The name of the widget
Description: What data the widget displays
Use case: When and why to use the widget
Interpretation: How to read and act on the data shown

Use this reference when selecting widgets for a dashboard or when reviewing dashboard data.

Widgets are grouped into categories based on their purpose to make navigation easier. For steps to add widgets to a dashboard, see Manage Custom Dashboard.

Executive Insights & Release Readiness

Widget	Description	Use Case	Interpretation
All Statuses Summary	Summary of test run counts across all result statuses: Passed, Failed, Error, Stopped, Cancelled	Get an instant overview of test execution health at a glance	High Passed count with low Failed count indicates good health. High Error count suggests infrastructure issues.
Pass Fail Trends	Historical volume of passed versus failed test runs over time.	Track quality trends and detect regressions during release cycles	Rising failed tests indicate quality decline. Upward trend shows improving quality. Flat pattern indicates stability.
Latest Runs	Most recent test executions with build numbers, timestamps, and status. Real-time updates as executions complete.	Monitor ongoing test execution and verify CI/CD pipeline activity	Frequent recent entries show active automation. Gaps in timestamps may indicate pipeline delays or infrastructure issues.
Consolidated Project Health	Aggregate health view showing pass, fail, error, stopped, and cancelled counts across all projects	Compare test health across your entire product portfolio in one view	High pass percentage indicates overall portfolio health. Identify which projects have high failure rates for targeted improvement.
Total Test Plan Runs	Cumulative count of all test plan executions in the date range. Large number KPI tile.	Measure testing volume and track execution frequency over time	Higher numbers show greater test coverage. Compare across periods to see whether testing intensity is increasing.
Test Plan Execution Analytics	Per test plan metrics: success rate, average duration, last run, and total executions. Sortable data table.	Identify slow test plans and understand which plans run reliably	Low success rates indicate flakiness. High durations reveal performance bottlenecks. Sort by duration to find slowest plans.
Pass Fail Percent Trend	Success rate percentage tracked over rolling time intervals. Formula: (successful runs ÷ total runs) × 100	Monitor whether test suite health is improving, stable, or declining	Upward line shows quality improvement. Downward line indicates regression. Flat line shows consistent maturity level.
Project Level Stats	Pass/fail counts and success rate aggregated by project	Quickly compare test health across different projects	Each project card shows overall health. Compare across cards to identify problem areas needing attention.

Reliability & Stability

Widget	Description	Use Case	Interpretation
Test Health Snapshot	Test suite classification: Consistent, Flaky, or Unexecuted (based on recent run history). Donut chart with percentages.	Quickly assess overall test suite stability at a glance	High Consistent percentage (ideal > 80%) shows a healthy suite. Large Flaky percentage signals stability work needed. High Unexecution may indicate coverage gaps.
Flaky Test List	Tests that flip-flop between pass and fail without code changes, ranked by Flake Score (higher score = more flaky)	Target high-impact flaky tests for stabilization and root cause analysis	Tests at top are most problematic. High flake scores indicate timing or resource-dependent issues. Start stabilization efforts with highest-scoring tests.
Top Failing Tests By User	Most frequently failing tests, segmented by the user who triggered the run	Identify which tests fail most and understand patterns by execution source	Failures consistent across users indicate real product defects. Failures only under specific users may indicate environment or data issues.
Top Failing Tests By Day	Most frequently failing tests, segmented by day of occurrence	Identify day-specific failure patterns	Failures concentrated on certain days may indicate scheduled maintenance, batch jobs, or data refresh cycles affecting tests.
Error Statistics	Count of test executions by error type: Idle Timeout, Network Error, Session Timeout, Device Not Available, etc.	Spot systemic infrastructure or environment issues causing failures	High Device Not Available suggests saturation. Network Errors indicate connectivity problems. Coordinate with infrastructure teams to resolve.
Top Error Types	Proportional breakdown of error type distribution	Understand the dominant failure mode and allocate investigation resources	The largest slice shows the most frequent error. If one type dominates, focus remediation there. Multiple similar sizes suggest distributed issues.
Time To Fix Test KPI	Average duration from first failure of a test to its successful resolution (in seconds)	Measure your team's efficiency in diagnosing and fixing test failures	Lower times indicate fast response. High times suggest slow root cause identification. Track trend to measure team improvement.
Stability Ratios	Success rate (%) grouped by number of attempts (1, 2, 3, 4+ attempts). Formula: (successful runs ÷ total runs) × 100	Understand how often tests pass on first try versus requiring retries	High 1st attempt success is ideal. If success improves on 2nd+ attempts, tests are flaky. Consistent success shows robust tests.

Test Design & Team Productivity

Widget	Description	Use Case	Interpretation
Tests Authored Over Time	Number of new test cases created in each time interval. Column chart.	Monitor test creation pace and ensure healthy coverage growth	Rising columns show increasing test creation. Flat or declining columns may indicate saturation or resource constraints.
New Tests Per User	New test cases created per team member, with step count. Leaderboard or bar chart.	Recognize high contributors and identify team participation gaps	High contributors drive coverage. Use to celebrate performance. Low contributors may need support or training.
Fixed Failed Tests Per User	Number of previously failing tests successfully fixed by each user	Credit test maintenance work and identify quality improvement drivers	High fixers are maintaining test health. If fixes are low team-wide, flaky tests may be accumulating.
Fixed Failed Tests KPI	Total count of previously failing test cases that have been fixed	Measure overall test stabilization progress	Rising numbers indicate successful stabilization efforts. Compare periods to track team maintenance effectiveness.
Tests Authored Vs Executed	Dual-axis comparison: tests created versus tests actually run. Dual-axis bar and line chart.	Detect inactive test cases and gaps between authoring and execution	If authored >> executed, inactive tests exist. Narrow gap shows healthy execution. Large gap signals stale tests.
Stale Assets By Category	Test cases and test plans grouped by inactivity duration	Identify orphaned tests and decide whether to maintain or archive	Large stale percentages indicate unused tests. Review why they stopped executing. Delete unused tests to reduce maintenance burden.
Distribution By Priority / Status / Type	Breakdown of test cases by priority level, current status, or test type	Understand test portfolio composition and ensure balanced coverage	Even distribution shows balanced coverage. Skewed distribution may indicate concentration. Adjust composition to match risk profile.
Authored By App Type	Number of test cases authored for each application type: Web, Mobile, API, SAP, and others	Verify balanced coverage across your platform portfolio	Similar bar heights show balanced effort. Large imbalances indicate heavy investment in some platforms with gaps in others.
Test Cases Submitted/Assigned For Review	Submitted: test cases you created awaiting review. Assigned: test cases assigned to you pending your review.	Track code review bottlenecks and manage QA approval workflows	High pending counts indicate review backlog. Low counts show smooth approval flow. Monitor to ensure timely review cycles.

AI Intelligence & Agentic ROI

Widget	Description	Use Case	Interpretation
AI Vs Manual Composition	Ratio of test steps created via AI versus manual recording/typing. 100% stacked bar chart.	Quantify AI adoption and measure shift from manual to AI-assisted authoring	Growing AI percentage shows adoption momentum. Target: increase AI composition month-over-month to show adoption velocity.
Auto Healing Trend	Failed steps vs. auto-healed steps over time	Demonstrate cost savings from AI-driven locator and image updates	High healing numbers show significant automation saving. Each healed test = manual effort avoided. Rising trend justifies continued AI investment.
NLP Usage Ranked List	Most frequently used natural language prompt templates in test step creation	Understand which AI capabilities teams leverage most and where training is needed	Top prompts show popular features. Low-usage prompts identify underutilized capabilities. Target training on low-use features.

Infrastructure & Performance

Widget	Description	Use Case	Interpretation
Concurrency Utilization Sessions	Concurrent parallel sessions in use versus the configured limit. Overlapping area chart.	Identify whether concurrency is underutilized or bottlenecked	Gap between used and limit shows unused capacity. Using near limit constantly suggests saturation. Plan expansion based on peak usage.
Execution Duration Trend	Average, median, and p95 test execution duration over time. Line chart with trend lines.	Detect slow test runs and performance regressions	Rising trend indicates slowing execution. Sudden spikes signal performance regression. Investigate prolonged runs to find bottlenecks.
Environment Coverage	Test run distribution by browser type, OS version, and mobile device model. Tree map or nested chart.	Ensure balanced testing across supported platforms and identify coverage gaps	Balanced boxes show even coverage. Large dominance by one environment may leave others undertested. Match coverage to customer distribution.
App Coverage By Type	Number of test runs per application type. Donut or pie chart.	Verify testing effort is proportional across your app portfolio	Similar slices show balanced effort. Large imbalance means some apps receive less testing. Adjust allocation to match risk profiles.

Quick Dashboard Templates

Use these recommended widget combinations as starting points when creating a new dashboard.

Release Readiness Dashboard

All Statuses Summary
Pass Fail Trends
Latest Runs
Consolidated Project Health

Quality Improvement Dashboard

Flaky Test List
Top Failing Tests By User
Error Statistics
Time To Fix Test KPI

Team Productivity Dashboard

Tests Authored Over Time
New Tests Per User
Fixed Failed Tests Per User
Stale Assets By Category

AI Investment Dashboard

AI Vs Manual Composition
Auto Healing Trend
NLP Usage Ranked List

Infrastructure Planning Dashboard

Concurrency Utilization Sessions
Execution Duration Trend
Environment Coverage
App Coverage By Type

Did you find it helpful?