In our real-world case study, we needed a system that would create test data. This data would be utilized for different types of application testing. The requirements for the system stated that we need to create a test data set that introduces different types of analytic and numerical errors. Twelve different scenarios need to be tested against, and the data files need to contain or be able to contain data that will exercise those 12 tests. In addition, the system needs to create different files that mimic the data sets or files customers submit. There can be up to eight different data sets or files. Each record in each file needs to have a correlation ID or primary/foreign key value to match and link across records in the files. These correlation IDs can be kept in a text file that the system will read and assign along with the created output.
Then, the system needs to be able to create different amounts of records per file to mimic the number of transactions in the source system. The output of the system should be able to stress the end user application by producing different-sized test files. The requirement for the output is to be able to create files of 1000, 10,000, 100,000 and 1,000,000,000 records.
Lastly, the system needs to keep track of the number of records in each file, the time it takes to create the output, the time it takes to process, the number of errors created per output test file by the 12 different test types, the number of errors correctly captured by the automated tests and other business-specific metrics. Some of these data points will come from the agentic AI system and some will be generated from the automation testing system.
