Faster Code, Deeper Debt? A Multivocal Literature Review on Technical Debt and Its Early Signs in LLM-Assisted Software Development

2026-06-11

AI Summary

This study addresses the technical debt introduced by large language model (LLM)-assisted programming, which—while enhancing development efficiency—can incur both traditional and LLM-specific forms of debt, such as prompt, ethical, data, and provenance debt, thereby exacerbating long-term maintenance burdens. Through a multivocal literature review encompassing 31 academic and 73 gray literature sources, the work systematically identifies the causes, manifestations, and early indicators of LLM-related technical debt, and introduces novel categories such as “rapid integration debt,” revealing its cascading effects on governance debt and rising costs. Building on these insights, the study proposes a classification framework for LLM-induced technical debt and evaluates existing mitigation strategies—including prompt engineering and human-in-the-loop mechanisms—using tools like SonarQube and CodeSmellEval, while highlighting the current lack of standardized metrics and benchmarks to guide future research and engineering practice.

Abstract

With the rapid adoption of LLM-assisted coding, the need to manage the technical debt these systems introduce has become urgent. In this paper, we conduct a multivocal literature review of 104 sources (31 formal, 73 grey) to examine how LLM-assisted development contributes to technical debt and what strategies, metrics, and benchmarks exist to mitigate it. We find that LLMs often amplify traditional forms of technical debt, particularly code, design, and documentation debts, while also introducing new LLM-specific debts. Notably, we identify fast-integration debt, where rapidly generated code prioritizes speed over quality, triggering a domino effect that leads to governance debt and increased long-term maintenance costs. Additional emerging categories include prompt, ethical, data, and provenance debt, reflecting new challenges unique to LLM adoption. To address these, strategies suggested in the literature include human-in-the-loop frameworks, prompt engineering, and data quality alignment. In practice, tools such as SonarQube are commonly used to detect technical debt indicators, while research prototypes such as CodeSmellEval are emerging to assess how LLMs contribute to debts. However, no standardized benchmarks or LLM-specific metrics yet exist, leaving an important gap. Based on findings, we outline insights and future directions to ensure reliable integration of LLMs into software engineering workflows.

Problem

• technical debt
• LLM-assisted software development
• fast-integration debt
• prompt debt
• governance debt

Innovation

• technical debt
• LLM-assisted development
• fast-integration debt
• prompt debt
• provenance debt