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arXiv · 2609.04218

A Governance Methodology Layer for AI-Assisted Software Development: Defect Taxonomy, Controlled Ablation, and a Test of Process-Over-Capability

Sungjin Kwon
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Abstract

Autonomous coding agents produce output that passes syntactic checks -- compilation, type safety, CI -- at high velocity. Yet syntactic correctness does not imply semantic correctness: design boundaries, security invariants, and maintainability contracts remain structurally invisible to automated pipelines. This paper makes four contributions. First, we present a defect-class taxonomy grounded in five AI agent permission and governance modules, separating defects structurally detectable by static analysis from those requiring semantic review. Second, we describe a runtime-decoupled governance gate -- a file-based protocol that reads generator output and emits a structured verdict without API coupling, hence portable across generators. Third, we formalize methodology-as-code: expressing a verification protocol as a version-controlled, executable artifact whose two layers separate portable methodology from host automation. Fourth, we report a controlled ablation experiment (E-ablation, N=5 artifacts, 8-item independent ground truth) comparing harness-structured review against a token-matched unstructured prompt. The structured condition records 62% lenient recall against 50%, with 25% strict against 0%. A severity-grade differential reported earlier does not survive blind re-grading and is withdrawn (Sec. 6.6). An independent-session re-test with blind scoring does not replicate that contrast: the conditions differ by one strict hit in 24 (6/24 against 5/24), the structured aggregate again 25% and the unstructured 0% not recurring (Sec. 6.7). Both conditions miss document-quality defects identified by a human QA reviewer, indicating complementarity between structured AI review and human process inspection. The re-test does not distinguish structured review from a detailed unstructured prompt here, so process design as the dominant factor remains a hypothesis, not a result of this paper.

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Sungjin Kwon. 2026-07-01. A Governance Methodology Layer for AI-Assisted Software Development: Defect Taxonomy, Controlled Ablation, and a Test of Process-Over-Capability. https://arxiv.org/abs/2609.04218

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