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QA services for employee wellness platform

DeviQA modernized GoodShape’s backend and frontend test automation, reducing defects after system updates by 95% and making testing and release cycles 3× faster.

QA services for employee wellness platform

Technologies and tools

Jira

Confluence

BrowserStack

Python

JavaScript

Robot Framework

Bitbucket

Behave Framework

Allure

Docker

Jenkins

Selene

Selene

Team

3 automation QA engineers

Project length

Since 2019

~3.8k

Automation test scripts created

2x

Faster regression testing run

~95%

Test coverage

15

Parallel threads

70%

Reported blocker/critical/
major/minor bugs

About project

GoodShape develops workforce well-being and organizational performance software. The company combines clinical expertise with technology to help large organizations manage employee health and workplace performance.

The GoodShape platform supports employee absence management, medical consultations, and workforce analytics. More than 200 employers across the UK use the platform.

Before DeviQA

  • Outdated BE autotests

  • Only smoke tests were automated on BE

  • No autotests on FE

  • Manual smoke testing on FE

  • Manual release testing

  • Outdated Python version and test framework

With DeviQA

  • All existing autotests are up-to-date

  • ~2800 autotests added

  • >90% of test cases are automated

  • ~99% of smoke tests are automated

  • ~95% decrease in post-release regression bugs, the implementation of test automation has proven to be highly effective

  • Fixed and updated test suite after major Python version upgrade

Our engagement

DeviQA joined the project to update the existing backend automation suite and establish frontend test automation.

The team refactored and stabilized the existing backend automated tests.

In parallel, frontend automated tests were created and integrated into a continuous integration and continuous delivery (CI/CD) pipeline using Jenkins and Docker containers. As the platform moved to the new setup, the automation suite was expanded to provide faster test execution and earlier defect detection.

Defects appearing after updates to the core system decreased by 95%. Testing and release cycles became 3× faster.

The team returned for a later phase focused on updating and expanding the automation setup. Existing automated tests were updated for a major Python upgrade, test coverage was expanded, and a smoke test suite was configured to run daily.

Services provided

Services

Web automation testing

The team created more than 3,800 automated test scripts for the GoodShape platform. Frontend automated tests were integrated into the continuous integration and continuous delivery (CI/CD) pipelines. Multithreaded execution increased the speed and stability of the automated test suite.

Automated regression tests covered approximately 95% of the application.

Facing similar challenges to GoodShape?

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