Intelligent batch job testing

A machine-learning based batch job testing system configured to generate and execute batch job test cases based on a natural language testing request, the system comprising:

• machine-readable storage configured to store an optimized file layout corresponding to a batch job source file, the optimized file layout generated based on sample file metadata using one or more machine-learning algorithms;
• and
• a processor configured to:
• receive a natural language request for a batch job test and identify one or more tokens within the request;
• based at least in part on one of the tokens, generate a scenario graph, the scenario graph comprising a plurality of scenario graph nodes;
• based at least in part on a scenario graph node and based at least in part on domain data comprising one or more finite states associated with the scenario graph node, generate one or more test cases;
• based at least in part on the optimized file layout, map the batch job source file to identify batch job data and execute the test case;
• and
• update a rule engine based on test results associated with the executed test case, the rule engine comprising one or more decision trees corresponding to one or more executed test cases.

One or more non-transitory computer-readable media storing computer-executable instructions which, when executed by a processor on a computer system, perform a method for generating and executing batch job test cases based on machine learning, the method comprising:

• receiving a natural language request for a batch job test;
• generating a scenario graph, the scenario graph comprising a graphical representation of a test scenario, the test scenario based at least in part on a keyword extracted from the natural language request;
• generating one or more test cases, each test case based at least in part on the scenario graph;
• mapping a data file to identify batch job data associated with a test case, the mapping based on an optimized file layout corresponding to the layout of the data file;
• executing the test case using the batch job data;
• and
• based on execution of the test case, compiling batch job test results;
• updating a machine learning algorithm with one more test case requirements, the test case requirements based at least in part on the test results.