Case Study

Insurance firm's end-to-end QA implementation

Client

Customer is one of the fastest growing life insurance companies in India

Application

eApp , Employee APP , Customer facing Portal for the end users

Team Size

  1. 4 Manual Testers
  2. 1 Automation Testers
  3. 1 BA resource

Industry

Insurance

Tech Stack

API , Hybrid Mobile App

Resource Distribution

Onsite

Duration

Ongoing

Types of Validation

  • Setup of Mature QA Processes
  • Manual Validation
  • Test Automation

Recommendations

More robust test data More coverage through test automation

Problem Statement

Due to lack of proper reporting mechanism and QA check posts the quality of deliverables were not transparent to business stakeholders. Also, absence of basic QA processes was affecting the overall health of the project

Challenges

  • Frequent and unplanned release made testing adhoc and inefficient
  • Functional Mobile testing on Android & iOS was taking too much time and slowing down the releases
  • Absence of unit testing and High bug count was leading to multiple iterations of defect fixing and affecting the product health
  • Unplanned and poor quality releases were resulting in UAT release date slippage and eventually delayed production releases and high cost of development

What we did

  • Conducted a detailed analysis of customer's problem areas (discussion with team members, SPOC of the project) to identify key QA processes needed.
  • Introduced release mechanism and framework for unit testing, which reduced the number of initial bug counts and release iterations
  • Understood their existing testing process and identified key areas for introducing Selenium & Appium automation
  • Prepared regression test scripts with selenium to ensure the product stable before launch on production

Outcome

  • Overall duration of UAT was shortened
  • Able to achieve management commitments by introducing best practices
  • Multiple systems were tested when the build was pushed to production
  • Quality of product delivery was better in comparison to the earlier delivery
  • 30% reduction of overall efforts post automated scripts were in place
  • 25% increase in productivity due to early defect detection