Claims of Novelty & Technical Disclosure

The system discloses a method for generating semantically identical but syntactically distinct software modules by:

• 1Ingesting a “Golden Specification” defined in Object Constraint Language (OCL) combined with Natural Language intent.
• 2Decomposing this specification into distinct Context Prompts for a Generative AI model (LLM).
• 3Injecting OCL Pre-conditions, Post-conditions and Invariants directly into the System Prompt of the LLM.
• 4Forcing the LLM to map these constraints into language-specific assertion logic (e.g., assert in Python, assert() macro in C, assert keyword in Java) during the generation phase, ensuring the generated code is “Design-by-Contract” compliant by default.

Unlike traditional testing which compares Output vs. Static Expected Result, this system discloses a method for Dynamic Truth Discovery by:

• 1Simultaneously executing differing runtime environments (e.g., a compiled C binary, a Java Virtual Machine instance and a Python interpreter) within isolated sandboxes (containers).
• 2Injecting identical Input Vectors into these environments via standard streams (STDIN) or argument parsing.
• 3Normalising the Output Streams (STDOUT) and Standard Error Streams (STDERR) of these diverse runtimes into a unified data structure.
• 4Arbitrating validity based on a Consensus Engine that treats the “Majority Vote” of the runtimes as the “Ground Truth,” rather than a pre-defined static value.

This claim discloses the specific mechanism of Automated Anomaly Correction:

• 1Identifying a “Dissenting Implementation” (a version of the code that disagrees with the Majority Vote).
• 2
  Constructing a “Remediation Prompt” that contains:

  • • The Original Code of the dissenting implementation
  • • The Input Vector used
  • • The Consensus Output derived from peer implementations (the “Target State”)
  • • The Divergent Output produced by the dissenting implementation (the “Error State”)
• 3Submitting this Remediation Prompt back to the LLM to generate a “Hot Patch.”
• 4Re-compiling and re-executing the patched module until it aligns with the consensus.

The system leverages an Observability Stack (specifically ELK: Elasticsearch, Logstash, Kibana/Grafana) to quantify the reliability of Generative AI models by:

• 1Indexing “Voting Logs” which capture the boolean agreement status of every execution.
• 2Calculating a “Hallucination Rate” per language (e.g., frequency of Python deviation vs. C deviation).
• 3Calculating a “Convergence Velocity” metric, defined as the number of recursive feedback loops required to achieve unanimous consensus for a specific OCL specification.

The disclosure of a specific architectural pattern wherein:

• 1No single implementation is trusted with “Write” access to a production database or critical system state.
• 2A “Gatekeeper Proxy” sits between the implementations and the external world.
• 3The Gatekeeper only commits a transaction if and only if a Quorum (N > 50%) of the isolated implementations request the exact same transaction with the exact same parameters.