When Clean Code Becomes Costly: Balancing Human Readability and AI Digestibility with the NUDEE Framework
This study maps the structural tension between human-centric Clean Code conventions and the architectural demands of LLM-based coding agents. It proposes NUDEE, a management framework for governing codebases optimized for both human readability and AI digestibility.
Clean Code prescribes deep abstraction and module decomposition to reduce human memory load. By 2026, LLMs and autonomous agents actively participate in coding workflows, but their token-limited context windows are penalized by the same decomposition that helps human developers.
A PRISMA 2020-guided systematic literature review of three databases (Scopus, Web of Science, ACM Digital Library) used a three-pillar Boolean search strategy followed by four-stage screening. The resulting corpus of 89 records (1996–2026) was inductively coded into five thematic streams.
The paper presents a PRISMA-structured systematic literature review – to our knowledge, one of the first to systematically map the human-readability–AI digestibility tension in software engineering – documents the field’s organizational turn (44.9% of the literature addresses management frameworks) and introduces the NUDEE framework.
The dominant stream, hybrid and agentic SE management frameworks (44.9%), confirms that the field has reframed the problem as organizational; synthesis across all streams reveals three structural pillars for AI-optimal codebases.
Development teams should adopt the Software Coach and System Orchestrator roles, instrument the Context Efficiency Index in CI/CD pipelines to detect semantic debt early, and revise code review criteria to include digestibility alongside correctness.
Empirical validation of the CEI against real agent task-solve rates in production codebases is the field’s most pressing research need.
As AI agents increasingly execute code in critical systems, semantic debt in organizational codebases directly translates into reliability and safety risk. NUDEE provides a governance vocabulary that enables mid-size organizations, not only large technology firms, to manage human AI software knowledge assets responsibly.
CEI empirical validation with threshold calibration and longitudinal case studies testing whether teams adopting the dual-role model achieve higher agent reliability and shorter release cycles.
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