Fiserv Auckland - Intermediate Software Test Engineer: Difference between revisions
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Fiserv Auckland - Intermediate Software Test Engineer (view source)
Revision as of 21:44, 25 July 2024
, 25 July→Custom Development and Integration
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The PTF automatically orchestrated the calls in the correct order to execute the user scenarios reliably. It used an external JSON file to specify a sequence of steps called userActions, each userAction referenced a request from the collection, and contained response handlers for each http response code which set the next userAction to perform. Effectively, the PTF was a simple state-machine. The PTF also implemented a simple nested JSON data syntax to be able to store data such as user credentials as well as FI connection settings. Passwords were encrypted when stored, and decrypted at run time. | The PTF automatically orchestrated the calls in the correct order to execute the user scenarios reliably. It used an external JSON file to specify a sequence of steps called userActions, each userAction referenced a request from the collection, and contained response handlers for each http response code which set the next userAction to perform. Effectively, the PTF was a simple state-machine. The PTF also implemented a simple nested JSON data syntax to be able to store data such as user credentials as well as FI connection settings. Passwords were encrypted when stored, and decrypted at run time. | ||
=== Custom Development and | === Custom Development and Build Pipeline === | ||
The PTF was implemented using [https://www.npmjs.com/package/newman '''Newman'''] in a [https://nodejs.org/en '''Node.js'''] project, with a custom JavaScript reporter developed to process events emitted by Newman during execution. This allowed for real-time capture of results and detailed logs, providing clear insights into failures and partial successes. Results were sent to the PTF dashboard, as well as to a dedicated [https://www.splunk.com/ '''Splunk'''] instance for comprehensive monitoring and analysis. The PTF dashboard and Splunk implementations are detailed in the sections below. | The PTF was implemented using [https://www.npmjs.com/package/newman '''Newman'''] in a [https://nodejs.org/en '''Node.js'''] project, with a custom JavaScript reporter developed to process events emitted by Newman during execution. This allowed for real-time capture of results and detailed logs, providing clear insights into failures and partial successes. Results were sent to the PTF dashboard, as well as to a dedicated [https://www.splunk.com/ '''Splunk'''] instance for comprehensive monitoring and analysis. The PTF dashboard and Splunk implementations are detailed in the sections below. | ||
A build pipeline was created in [https://learn.microsoft.com/en-us/previous-versions/azure/devops/all/overview?view=tfs-2018 TFS - Team Foundation Server] ''(now rebranded to Azure DevOps)'', with two VM's each running 10+ build agents. The pipeline was configured to execute the PTF concurrently in parallel, every hour, for all users. Environment variables were used to provide the PTF with FI settings and user credentials. | |||
The automation suite could be triggered from the Web UI, and code commits to the automation suite itself also triggered an execution. | |||
The VM's were set up and configured with Splunk and kept real time test results, extensive console logging tied to individual tests, as well as full API request and response traces. | |||
=== Development of PTF Dashboard === | === Development of PTF Dashboard === | ||