Abstract: A system and method include reception of a prompt, determination that the prompt is not semantically similar to any of a plurality of text generation model prompts, in response to the determination, prompt a first text generation model to determine whether the prompt is malicious, in response to a determination that the prompt is not malicious, prompt a second text generation model with the prompt to determine a first prompt output, prompt a third text generation model to determine whether the first prompt output is malicious, and, in response a the determination that the third text generation model determined that the first prompt output is not malicious, return the prompt output in response to the prompt.

Abstract: The present disclosure involves systems, software, and computer implemented methods for system provisioning. One example method includes receiving a request for provisioning of a software solution. A knowledge graph is accessed that comprises a graph of object types of sample data and dependency information for the object types. The knowledge graph is traversed to identify object types and object dependencies included in the software solution. An interface is invoked to determine whether a data repository includes, for each identified object type, data for the identified object type. In response to determining that the data repository includes data for each identified object type, the interface is invoked to iteratively retrieve, in a dependency order determined based on the object dependencies of the identified object types, data from the data repository of each identified object type. Retrieved data is provided, in the dependency order, for deployment during provisioning of the software solution.

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: A system and method include determination of input fields of automation scripts, acquisition of metadata of database object fields, prompting of a text generation model using a chain-of-thoughts prompt, the input fields and the acquired metadata to determine mappings between the input fields and the database object fields, prompting of an embedding model to generate embeddings based on each input field, associating each embedding with each mapping that includes the input field on which the embedding was generated, identification of a first input field of an automation script, prompting of a second embedding model to generate a first embedding based on the first input field, searching for embeddings similar to the first embedding, identification of a candidate mapping associated with each of the embeddings, and determination of a first database object field to bind to the first input field based on the identified candidate mappings.

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.