Code modernization

A system comprising:

• one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• parsing a legacy code base to identify one or more data flows between modules executing on a legacy production system;
• capturing, based on the identified one or more data flows, production data inputs and expected legacy outputs generated by the legacy production system executing the legacy code base to generate one or more backtest packets;
• generating, by a large language model (LLM) agent using a current system context, a modern code candidate for a legacy module executing on the legacy production system;
• executing the modern code candidate using the production data inputs from one or more backtest packets to generate modern outputs for the modern code candidate;
• comparing the generated modern outputs to one or more expected legacy outputs from one or more backtest packets to determine whether the modern code candidate has behavioral equivalence with the legacy module;
• in response to determining that behavioral equivalence has not been achieved, computing one or more discrepancies between the generated modern outputs and the expected legacy outputs;
• and
• generating an updated system context including adding the computed discrepancies to the system context for the LLM agent;
• and
• iteratively generating, by the LLM agent using the updated system context, an updated modern code candidate.

One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• parsing a legacy code base to identify one or more data flows between modules executing on a legacy production system;
• capturing, based on the identified one or more data flows, production data inputs and expected legacy outputs generated by the legacy production system executing the legacy code base to generate one or more backtest packets;
• generating, by a large language model (LLM) agent using a current system context, a modern code candidate for a legacy module executing on the legacy production system;
• executing the modern code candidate using the production data inputs from one or more backtest packets to generate modern outputs for the modern code candidate;
• comparing the generated modern outputs to one or more expected legacy outputs from one or more backtest packets to determine whether the modern code candidate has behavioral equivalence with the legacy module;
• in response to determining that behavioral equivalence has not been achieved, computing one or more discrepancies between the generated modern outputs and the expected legacy outputs;
• and
• generating an updated system context including adding the computed discrepancies to the system context for the LLM agent;
• and
• iteratively generating, by the LLM agent using the updated system context, an updated modern code candidate.