mlsanityData to 7 Oct 2026

Protocol-Driven Development: Governing Generated Software Through Invariants and Continuous Evidence

arXiv 2605.12981 ↗First posted 13 May 2026cs.SE cs.AI cs.LGcited by 2

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Abstract (arXiv)

Automated program synthesis lowers the cost of producing implementations but introduces a harder governance problem: determining which generated artifacts are admissible. Natural-language specifications are ambiguous, and example-based tests sample only part of the behavioral space. Used alone, neither provides a sufficient control boundary. We introduce Protocol-Driven Development (PDD), where the primary software artifact is a machine-enforceable protocol rather than code. We define a protocol as the triplet P = (S, B, O), specifying structural, behavioral, and operational invariants. Their conjunction defines the admissible implementation space of a software component. Under PDD, implementations are replaceable realizations discovered through constrained search. An implementation is admitted only if it satisfies the protocol and produces a verifiable Evidence Chain of compliance. Admission is grounded in protocol satisfaction and recorded evidence rather than trust in the generator. For deployed systems, we extend the Evidence Chain into a Dynamic Evidence Ledger. Runtime verifiers append signed observations, invariant checks, and violations to the ledger, allowing monitorable obligations to be continuously attested. This connects live failures back to the generation loop without granting the generator runtime authority. Combining formal methods, property testing, runtime verification, policy-as-code, and software provenance, PDD defines a governance model for automated software engineering. Its organizing principle is that code is transient, while the protocol carries durable authority.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 3 of its 57 references that are in the map. First posted on arXiv 13 May 2026.

Builds on

3 of 57 references.

References · 3

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  3. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264

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Citing papers per year: 2026: 2.

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