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QA for image-based social network

DeviQA established the QA process and built a browser test automation solution for We Heart It from scratch, reducing full-suite execution from 12 hours to 2 hours.

QA for image-based social network

Technologies and tools

WebDriver.io

JavaScript

Linux

Jenkins

Multithreading

BrowserStack

REST API

Bamboo

Xcode

Android Studio

JMeter

Team

3 automation QA engineers

Project length

1 year

About project

We Heart It is an image-based social network for discovering, collecting, organizing, and sharing visual content. Users can “heart” images, share them with other users, and organize saved content into collections. The platform supports content discovery and interaction around shared visual interests. We Heart It provides mobile applications for iOS and Android.

Before DeviQA

  • There was no formal QA process on the project

  • Manual regression testing took about 2 weeks

  • There was no well-structured system for test result collection and analysis

With DeviQA

  • A complex architectural solution has been designed for automated tests from scratch, and ≈ 2,000 test scripts were developed

  • 15 virtual machines were run on a dedicated 64GB RAM server to enable parallel test execution on 40—45 browsers in total

  • Automated regression testing took only 2 hours

  • Our automated web tests were integrated with bug tracking and team management systems

  • The statistics about all test runs were stored on the CI side

Our engagement

DeviQA's primary objective was to cover 95% of the application with automated tests and provide reliable information for deployment decisions.

The team designed a test suite architecture that supported multithreaded execution across six browsers. Atomic test scenarios prevented data and execution conflicts between parallel tests. The architecture also evaluated API-based setup and direct database creation of test entities to reduce repeated preparation steps.

Because the project had no formal QA process, the team defined the testing workflow, introduced it across the project, and explained the new procedures to other team members.

The automated test environment was designed for high-volume parallel execution. The team configured 15 virtual machines on a dedicated server with 64 GB of RAM. Each virtual machine ran three browser instances in parallel. The infrastructure supported parallel test execution across 40–45 browser instances. Multithreaded execution reduced the time required to run 2,000 test scenarios from 12 hours to 2 hours. This sixfold reduction gave the product team faster feedback for deployment decisions.

Automated tests covered six browsers and multiple browser versions. The web automation suite was integrated with the project's bug-tracking and team management systems. After each run, detected defects were automatically recorded in the bug-tracking system, while the corresponding test cases were updated with passed or failed statuses.

The continuous integration (CI) system stored test execution statistics and maintained the history of previous builds and test runs. After each execution, the system generated and distributed a report detailing passed and failed test scenarios.

Services provided

Services

Performance testing

After the initial phase of the engagement, part of the QA team conducted performance and load testing. This one-time assessment evaluated the application's speed and stability under load.

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