Code Comprehension Gaps in AI-Assisted Development: A Conceptual Framework for Knowledge Erosion Patterns
Authors’ abstract
The increasing adoption of AI-powered code generation tools has introduced a critical challenge in software engineering, the potential erosion of developer comprehension of codebases increasingly composed of AI-generated artifacts. Before examining this erosion, we establish that human code comprehension remains a functional requirement in AI-augmented development on four independent grounds: regulatory accountability obligations, the need for human oversight of AI failures, organizational resilience against tool unavailability, and the causal reasoning demands of incident response. This paper examines how AI-assisted development may create knowledge gaps within engineering teams by presenting a conceptual framework for understanding code ownership levels and proposing a taxonomy of knowledge voids that may emerge in AI-augmented development. We propose theoretical models for understanding how AI-generated code may differ from human-written code in ways that affect long-term comprehension and maintenance. Our analysis leads us to hypothesize that AI-generated code may exhibit patterns including lower explanatory documentation, different structural complexity characteristics, and greater implementation diversity, factors that could increase comprehension challenges during maintenance. We conclude with hypothesis-driven implications for development practices and identify critical needs for longitudinal research to validate these theoretical concerns in real-world development contexts.
Paper
- Authors
- M.S. Patel, R. Dhawan, L. Bramhanwade, D.J. Mishra
- Publication date
- 2026
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- Source license
- Creative Commons Attribution 4.0 International
- Recording license
- CC-BY-4.0
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