Protocol-Driven Development: Governing Generated Software Through Invariants and Continuous Evidence
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Program SynthesisSimilar Papers 제목 키워드 기반
E2Edev: Benchmarking Large Language Models in End-to-End Software Development Task
The rapid advancement in large language models (LLMs) has demonstrated significant potential in End-to-End Software Development (E2ESD). However, existing E2ESD benchmarks are limited by coarse-grained requirement specif…
Spec-Driven Development:From Code to Contract in the Age of AI Coding Assistants
The rise of AI coding assistants has reignited interest in an old idea: what if specifications-not code-were the primary artifact of software development? Spec-driven development (SDD) inverts the traditional workflow by…
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Language technologies play a key role in assisting people with their writing. Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due t…
Grammatical Error CorrectionAI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. This paper presents a systematic audit of t…
Code GenerationEvaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios
In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, …