Abstract: A system and method include operation of a chatbot application to receive a first identifier of a data type, a second identifier of a data object, and a third identifier of a target system, generation of a first multi-dimensional vector based on the identifiers, searching of a vector database of multi-dimensional vectors to identify a second multi-dimensional vector similar to the first vector and a candidate data object instance corresponding to the second vector, prompting of a text generation model to output an indication of whether or not the candidate data object instance is usable in the target system, and, in response to an indication that the candidate data object instance is usable, calling of an application programming interface of the target database system to store the candidate data object instance in the target system.

Abstract: A computer-implemented method for improving test data troubleshooting in an enterprise resource planning (ERP) system is disclosed. The method can receive an error message generated by an application programming interface of the ERP system when processing a set of test data, embed the error message into a message vector, search a vector database comprising a plurality of object vectors to identify a target object vector matching the message vector, retrieve a target object document associated with the target object vector, and generate an answer including instructions to modify the set of test data based on the error message and the target object document. Related systems and software for implementing the method are also disclosed.

Abstract: According to some embodiments, systems and methods are provided including an object data store storing object data for each object, the object data including one or more properties; a template data store storing one or more templates associated with the object data; a memory storing processor-executable program code; and a processing unit to execute the processor-executable program code to cause the system to: retrieve information from the object data store for a first object, including available properties for the first object; define the first object with at least one retrieved available property, the defined first object is a template candidate; generate simulated test data with the template candidate; and store the template candidate as a template in the template data store. Numerous other aspects are provided.

Abstract: According to some embodiments, systems and methods are provided including a test data repository storing test data; a memory storing processor-executable program code; and a processing unit to execute the processor-executable program code to cause the system to: change a state of the stored test data from a first state to a second state in response to execution of test executable code, the execution using the first state test data; store the second state test data; detect a difference between the first state test data and the second state test data by comparing the stored second state test data to the first state test data; and restore the stored second state test data to the first state test data. Numerous other aspects are provided.

Abstract: A system and method include reception of a data object template comprising a plurality of fields and a respective value for each of the plurality of fields, determination of at least one master data-dependent field of the plurality of fields, generation of a plurality of data instances comprising the respective value for each of the plurality of fields except for the at least one master data-dependent field, where a value of the at least one master data-dependent field in each of the plurality of data instances is different from the respective value of the at least one master data-dependent field in the data object template, input of each of the plurality of data instances into a machine learning model to determine a likelihood of successful deployment for each of the plurality of data instances, and determination of a second plurality of data instances for deployment based on the determined likelihoods.