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This video demonstrates how a diagnostics agent can automate the process of diagnosing failed tests and suggesting maintenance fixes, significantly reducing the manual effort involved. ✅Diagnosing Failures: 🔹The video begins by showing a failed test, where a person would traditionally scroll through screenshots to find the failed test step. 🔹Tools available for diagnosis include log information, the console, and a screenshot comparison tool. The system can even record videos of the entire runs. 🔹In the example shown, the failure is apparent: the phone number and fax fields are missing from the application state. 🔹Traditionally, a person would manually write a root cause analysis and send it to a developer so they can adjust things on the application side. 🔹The diagnostics agent can perform a lot of this "heavy lifting in true autonomy". 🔹The agent works by looking at screenshots, machine learning data, and all the information captured during execution, then directly comparing it to results from prior test runs. 🔹This information is used to perform a root cause analysis. 🔹In the example, the agent identifies the root cause: the phone number and fax fields are missing from the account information section, which highlights the "structural awareness" of the agent. ✅Application Failure Workflow: 🔹If the failure is an application failure, the goal is to get that information to developers as quickly as possible. 🔹The video notes that the gap between QA teams and developers can really slow down the application release cycle. 🔹For an application failure, the user can take the diagnostic report and create a new defect. 🔹The system has out-of-the-box integrations for task tracking systems, allowing users to report the issue to platforms like Jira. 🔹The report includes the full diagnostic report and a link back to the test run. 🔹Since the product does not license per user, developers, stakeholders, and team leads can directly access the reports from these tests. This helps with closing the gap between QA teams and developers long-term. ✅Test Case Maintenance: 🔹If the failure is a test case failure, as opposed to an application failure, the user can view "maintenance suggestions". 🔹The system will supply fixes that can be applied directly to the test case. 🔹These fixes often update the associated test data. For example, a fix might update the test to look for the website field instead of the phone field. 🔹The agent can also supply configuration changes. This can be used to adjust the test configuration to account for slowness in a lower environment, rather than having a wait in place. 🔹These configuration changes can be directly applied using the agent. 🔹This provides numerous options to support tests long-term without going through a lengthy diagnostics and maintenance cycle for every new test. ➡Like the video! ➡Share this video with your friends! ➡Please subscribe if you enjoyed this video! 👉Subscribe to our channel:@Functionize Make sure to hit the BELL icon to get notified of new videos! 🔔 🌐Learn more: https://www.functionize.com Chapters: 00:00 Introduction to the Failed Test Scenario 00:18 Reviewing Screenshots and Logs Manually 00:40 Identifying the Visible Failure 00:57 Traditional Manual Root Cause Analysis 01:10 Introducing the Diagnostics Agent 01:28 Running the Diagnostics Agent Live 01:50 Automated Root Cause Detection 02:10 Creating a Defect from Diagnostics Report 02:35 Closing the Gap Between QA and Dev Teams 02:56 When It’s a Test Case Failure (Not App Failure) 03:10 Automated Test Maintenance Suggestions 03:33 Applying Suggested Fixes Automatically 03:55 Conclusion: Faster Diagnosis & Maintenance #TestAutomation #SoftwareTesting #QATesting #AutomatedTesting #DiagnosticsAgent #AIinTesting #RootCauseAnalysis #Functionize

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