The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development

TL;DR

This paper investigates the Productivity-Reliability Paradox in AI-assisted software development, emphasizing the importance of specification discipline over model capability for dependable outcomes.

Contribution

It formally defines the PRP, proposes a taxonomy of AI methodologies, introduces a governance model, and evaluates practical instantiations through a pilot study.

Findings

Specification discipline is crucial for dependable AI-assisted development.

The PRP is influenced by task abstraction, codebase maturity, and developer experience.

Governance models can mitigate the PRP's effects.

Abstract

Since 2022, AI-powered coding assistants have produced contradictory evidence: controlled studies report 20-56% productivity gains on well-scoped tasks, while the most rigorous RCT documents a 19% slowdown for experienced developers, and telemetry across 10,000+ developers shows 98% more pull requests but 91% longer review times with flat delivery metrics. This paper argues these findings constitute the Productivity-Reliability Paradox (PRP): a systematic phenomenon emerging from non-deterministic code generators and insufficient specification discipline. Through a multivocal literature review of 67 sources (2022-2026), this paper: (1) formally defines the PRP with three moderating variables (task abstraction, codebase maturity, developer experience) and two amplifying mechanisms (code review bottleneck, context window constraint); (2) proposes the AI-Augmented Methodology Taxonomy (AAMT), classifying six methodologies under three AI integration tiers; (3) introduces the Specification Governance Model (SGM), grounded in Transaction Cost Economics, with a practical governance decision guide; and (4) evaluates Spec Kit and TDAD as SGM instantiations via a four-month pilot study. Specification discipline, not model capability, is the binding constraint on AI-assisted software dependability.