Alexey Korzhenko

This hosted, cloud-based software application utilizes machine learning algorithms to identify clinicians whose behavior indicates a higher risk for drug diversion and helps solve the opioid crisis.

The project aimed to gather all QA requirements to establish MQA and AQA processes. To achieve this, I conducted several onsite and offsite meetings, collected all necessary details, and described the process in Confluence. I also shared the process with the whole team and customers. It helped to release the project in time and improve the project quality.

The project aimed to improve automation coverage. To achieve this, I proposed to cover existing APIs with integration tests using Postman. Also, I prepared integration checklists and automated them. We attached these tests to our CI/CD pipeline and executed them in a post-deployment step. As a result, the tests were performed after every command, and quick feedback was provided on the code.

In addition, I reviewed the existing functional and regression test cases, selected the priority ones, and automated them using Cypress.io. This has improved the automation coverage, and the regression tests are not time-consuming.

The project's aim was to improve integration test coverage. To achieve this, I was assigned to the project as a quality coach. My responsibilities were to review the existing integration tests, improve their structure, create additional tests, and teach the dev team to write the tests.

I successfully covered all my responsibilities: reviewed, updated, and created additional integration tests, which were executed on a built-basis giving feedback on every deployment. The dev team also started writing integration tests. As a result, the test coverage was improved.
The postman collections are linked as an example of the work completed.