The Unique Identiﬁcation Authority of India (UIDAI), established by the Government of India, is mandated to issue a unique identiﬁcation number (called Aadhaar or UID) with associated biometric data to all residents to allow them to identify themselves anywhere in India, and to access a host of beneﬁts and services.
Here is how Mindtree helped the UIDAI test a critical solution component that validated demographic and biometric data to ensure that a single ID is generated for each resident.
The customer wanted a product to validate resident demographic and biometric data. The team also wanted to implement continuous feature enhancement. This involved multiple releases per month and therefore multiple rounds of regression testing.
For each resident, data had to be veriﬁed for various functional ﬂows, demographic details such as age groups and PIN codes; and biometric information. This veriﬁcation needed to take place after decrypting encrypted source data. The customer also required a partner that could ensure accuracy and security of data transfer; and data integrity across multiple data stores.
Mindtree was tasked with providing testing services to ensure a robust end product, and streamline testing of its frequently updated feature set.
Mindtree put in place the JSystem open source framework to customize, develop and build an automation suite leveraging its centralization, scalability, user management and other advantages. We developed scripts to ensure master data is available and environment setup is completed for processing data packets and for smoother automation.
Automation was executed in multiple environments with minimal manual intervention.
Reusable utilities were derived out of commonly used functionalities and the testing suite modularized to increase its performance. The suite covered functional workﬂows with validation of varied data combinations. In all, there were over 500 ﬁeld-level validations spread across 31 diﬀerent modules. In addition, the automation suite was conﬁgured to ensure data integrity is maintained across diﬀerent six distinct data stores.
The JSystem framework was also customized to better meet the project requirements. It's reporting functionality was modiﬁed for better analysis and debugging of success and failure test cases. Reporting functionality was activated to provide detailed information on automation suite execution test statistics such as number of test cases passed per module. Additionally, the team assessed server stability by processing resident data in bulk, simulating the production environment.
Along the way, Mindtree met several technical challenges, including the need to verify integration between servers and to ensure there was no functional breakage in the existing modules. We also automated a majority of server features anticipating frequent releases with shorter release times.