16 Oct How effective QA process can increase productivity and speed up release cycle
Our client provides software enabling multi-channel retailing. They have an eco-system which enables sellers to sell on different ecommerce marketplace websites, apps and POS (Point of Sale). Manage. inventory and orders effectively without any hassle. Warehousing and order fulfillment for both B2B and B2C easily.
- Unstable Environment: Existing QA flow is as such, that each member will first test the changes/bugs by creating environment on their local machine. Which includes restoring of database on local MySQL Workbench and creating server to run application on local host. In second stage of testing, all changes/bugs code will merge, and QA’s again verify changes/bugs in Preprod environment in order to make sure that merge didn’t break anything. As the release cycle is of one week, problem here is there no time of QA to execute regression before Production due to these many issues remain undiscovered after the code was merged.
- Lack of Documentation: More verbal communication and keeping little reference material how the software has become over the period. There are no records of Test Data and Test cases in existing User Stories and Bugs.
- Restrictions of Server access for QA: QA’s seeking developer help for environment/data problems which basically stopping them to test the actual test points. QA could find the cause of new validation messages or generic errors as they cannot see logs.
- No Automation: No API automation implemented hence covering all API cases after changes in API’s were a hectic task.
We suggested and started having one environment for all QA’s. One Database and One URL for everyone to test from the first stage it-self. We started creating detailed Test Data and Cases to ensure the coverage. For every change QA expected a change document in proper format which helps then creating a effective Test Plan. QA team started accessing and review the logs files through WinSCP saving a lot of developer’s time and resolving configuration/data related issues own their own.
We automated testing of all API’s using in Pyhton using pytest framework and Allure for Reporting.
- Number of issues reported in first stage of Test Environments increases by 30% in the first Stage after the started creating Plan and Cases for every change and bug.
- Number of issues reported within a week of going live reduces by 9%.
- A knowledge base is created in a form of documents after we started documenting.
- Developers productivity increases as they don’t need to spent time with QA in order to resolve configuration and data issues.