In our real-world case study, we needed a system to create test data. This data will be used for various types of application testing. The requirements for the system stated that we needed to create a test data set that would introduce various types of analytical and numerical errors. Twelve different scenarios must be tested, and the data files must contain or be able to contain data that will exercise those 12 tests. In addition, the system must create various files that mimic the data sets or files submitted by clients. There can be up to eight different data sets or files. Each record in each file must have a match ID or primary/foreign key value to match and link between records in the files. These correlation IDs can be kept in a text file that the system will read and allocate along with the output created.

The system must then be able to create different amounts of records per file to mimic the number of transactions in the source system. The system output should be able to stress the end user’s application by producing test files of various sizes. The output requirement is to be able to create files of 1000, 10,000, 100,000 and 1,000,000,000 records.

Finally, the system must keep track of the number of records in each file, the time it took to create the output, the time it took to process, the number of errors created for each test output file by the 12 different test types, the number of errors correctly captured by automated tests and other business-specific metrics. Some of these data points will come from the agent AI system, and others will be generated from the automation testing system.