QAINFINITY / CASE STUDY / E-COMMERCE / WEB + MOBILE AUTOMATION

How QAINFINITY Cut Angara's Regression Cycle from 8 Hours to 30 Minutes

Angara's manual regression cycle consumed a full working day. Targeted quality engineering and test automation brought the web suite down to approximately 30 minutes.

Discuss regression automation
CLIENT SNAPSHOT
Angara

A fine-gemstone e-commerce brand with a personalized buying experience.

A full working day of manual regression stood between each release and the customer experience. Targeted automation turned that bottleneck into a repeatable release-confidence practice.

THE CHALLENGE

Manual regression was the release bottleneck.

Angara's release quality depended heavily on manual regression across critical web and mobile journeys. The work made it harder for QA to spend time on exploratory and complex-scenario testing where defects can hide.

  • Approximately 8 hours per regression pass. Every release demanded a full working day of repetitive manual execution before the team could ship with confidence.
  • Coverage was undocumented. Without formally documented test cases, knowledge gaps and inconsistent execution made a reliable automation foundation difficult to establish.
  • QA capacity was constrained by repetition. Manual effort crowded out exploratory and complex-scenario testing, limiting the team’s ability to investigate the risks behind each change.

THE APPROACH

Build the automation on a quality-engineering foundation.

QAINFINITY focused the work on the journeys that mattered most to release confidence, customer experience, and visual correctness across web and mobile.

Stakeholder-aligned test-case documentation
QA, development, and product perspectives shaped structured scenarios with clear objectives, preconditions, steps, expected outcomes, and acceptance criteria.
Critical web and mobile journeys automated
Sanity and regression coverage targeted critical functionality and high-risk user journeys across both platforms.
Visual verification included in the suite
Sikuli and OpenCV supported image-based interaction and visual comparison, checking the interface as customers experience it.
CI-integrated and parallel-ready execution
Jenkins integration and Allure reporting gave the team build-level visibility, while parallel-ready suites opened a faster execution path.

Tool context: Selenium, Appium, Sikuli, OpenCV, Jenkins, and Allure.

THE RESULTS

A full day became a repeatable release safety net.

The reported outcomes preserve the approximate nature of the source measurements. The manual baseline was approximately 8 hours for both web and mobile regression before automation.

Web regression
From manual baseline to automation
Manual baseline
Approximately 8 hours (full working day)
After — sequential
Approximately 30 minutes sequentially
After — parallel
Approximately 3 minutes in parallel
Mobile regression
From manual baseline to automation
Manual baseline
Approximately 8 hours (full working day)
After — sequential
Approximately 2 hours on a single device
After — parallel
Approximately 3–7 minutes in parallel
  • Faster, more repeatable regression supports more confident releases.
  • Automating repetitive checks gives QA more room for exploratory and complex-scenario testing.
  • Parallel execution provides a faster path without changing the suites.

WHY IT WORKED

The foundation made the speed credible.

The work did not simply automate an undocumented process. It first made coverage explicit, checked visual correctness as customers experience it, and connected execution to the team’s CI flow.

  • Documentation closed coverage gaps and created a shared basis for automation.
  • Visual checks protected the customer experience beyond locator-based checks alone.
  • CI integration and parallel-ready execution made the automation easier to scale with release needs.

NEXT STEP

Still bottlenecked by manual regression?

Discuss the journeys, coverage, and release risks your quality-engineering practice needs to handle next.

Discuss regression automation