Most test management tools provide some level of test reporting, covering common metrics such as test coverage, execution status, defect metrics, and velocity metrics.
However, these metrics, in their current shape and form, offer only basic information, leaving room for challenges:
Execution Status: While the execution status shows pass, fail, pending, and skipped results, it lacks crucial details about what specifically failed.
Execution Evidence: The absence or need to custom-build logs, screenshots, and videos, along with the necessity to use multiple tools like Jenkins, cloud providers’ dashboards, and other reporting tools for analysis, creates inefficiencies.
Test Flakiness: While some test management tools indicate test flakiness over time, this information is not always readily available.
Retry Indicator: Information on the number of attempts a test failed before passing after multiple retries is often missing.
Integration with Cloud Farms: Logs and graphic evidence from third-party cloud providers are not seamlessly integrated.
Requirement Stability Trend: Insights into the stability trend of requirements are often lacking.
Integration with Multiple Frameworks: Many test management tools have limited framework integrations or require custom-built integrations, leading to a fragmented view of test coverage.
Debugging, a time-consuming process during test execution, is hindered by the limited view of status, forcing teams to gather details from multiple tools and mine historical evidence for root cause analysis.
Test Coverage: The existing test coverage metrics indicate the number of tests for a requirement but fail to provide insights into the different types of tests required for adequate coverage.
Missing Criteria in Test Coverage: Key aspects like the coverage by test conditions, test types, and code coverage by the framework are often missing from common test management tools.
Defect Metrics: Though some tools show defect metrics and integrate with external tools like Jira, automated classification of defects based on root cause is absent, leading to manual and time-consuming classification.
Velocity Projection: Existing projections on completion of testing cycles lack consideration for complexity or AI mechanisms, resulting in unreliable projections.
At Digy4, we have identified these gaps over the years, and when building DigyDashboard, we focused on addressing these key areas to significantly reduce testing cycle times. All features and integrations on DigyDashboard are out-of-the-box, eliminating the need for custom integrations and streamlining your testing ecosystem.
Embrace DigyDashboard and revolutionize your test reporting. From high-level insights to detailed drill-down views, we have you covered.
Book your DigyDashboard demo today: https://digy4.com/digydashboard#dash-plans