System

A system comprising:

• circuitry configured to:
• receive, via a communication interface coupled to a packet-switched network, input data from a client terminal, wherein the input data comprises a natural-language token sequence having a maximum length of 512 to 2048 tokens;
• apply a tokenizer to the natural-language token sequence to generate a subword token array;
• input the subword token array into a Transformer-based neural network comprising an encoder layer having parameters numbering in a range of one billion to one hundred billion, the encoder layer extracting structural feature data comprising table identifiers, column identifiers, conditional operators, and join relationships from the subword token array;
• retrieve database schema information comprising structured data specifying table names, column names, data type information, and index information;
• generate, by mapping the structural feature data and the database schema information in a high-dimensional semantic space of the Transformer-based neural network, a structured query statement represented as an abstract syntax tree comprising a token sequence of query keywords, table references, column references, and conditional expressions;
• compute a confidence score for the structured query statement and compare the confidence score against a threshold value;
• and
• transmit, via the communication interface, response data comprising the structured query statement to the client terminal when the confidence score meets or exceeds the threshold value.

A method performed by circuitry, via a communication interface coupled to a packet-switched network, the method comprising:

• receiving specification data comprising requirement parameters and program data associated with a target module;
• generating, using a data generation model, modified program data based on the specification data and the program data, the data generation model comprising an encoder-decoder neural network that receives the specification data and an abstract syntax tree representation of the program data as multidimensional tensors and outputs the modified program data with an associated confidence score;
• performing a validation operation on the modified program data by applying at least one of static analysis or automated test execution to the modified program data;
• and
• generating a plurality of candidate program data sets by applying different inference parameters to the data generation model, each candidate program data set representing a distinct modification of the program data.