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

2026-06-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, extended

Summary

A multivocal literature review of 104 sources (31 formal, 73 grey) examines how large language model (LLM)-assisted software development impacts technical debt. The study reveals that LLMs not only amplify traditional forms of debt, such as code, design, and documentation debts, but also introduce novel LLM-specific categories like fast-integration, governance, prompt, ethical, data, and provenance debt. Fast-integration debt, for instance, arises from prioritizing speed over quality in rapidly generated code, leading to increased maintenance costs. While strategies like human-in-the-loop frameworks, prompt engineering, and data quality alignment are suggested, and tools like SonarQube are used, the review identifies a critical gap: the absence of standardized benchmarks and LLM-specific metrics to effectively manage this emerging technical debt.

Key takeaway

For Software Engineers and AI/ML Directors integrating LLMs into development workflows, you must proactively manage the amplified traditional technical debt and the new LLM-specific debts like prompt and governance debt. Prioritize human-in-the-loop review and robust prompt engineering to ensure code quality and alignment with system architecture. Without standardized LLM-specific benchmarks, rely on internal data and conservative validation to prevent long-term maintainability issues and escalating costs from rapidly integrated, unverified AI-generated code.

Key insights

LLMs accelerate traditional technical debt and introduce new, unique forms, necessitating novel management strategies and metrics.

Principles

• LLMs amplify existing code, design, and documentation debt.
• New debts like prompt and governance debt are LLM-specific.
• Human oversight and data quality are crucial for mitigation.

Method

A multivocal literature review of 104 sources (31 formal, 73 grey) identified debt types, mitigation strategies, tools, and metrics in LLM-assisted development.

In practice

• Implement human-in-the-loop code review for LLM outputs.
• Use prompt engineering to guide LLMs toward quality.
• Integrate static analysis tools like SonarQube.

Topics

• Large Language Models
• Technical Debt Management
• AI-Assisted Development
• Code Quality
• Prompt Engineering
• Software Maintainability

Code references

• primer/react

Best for: AI Architect, AI Scientist, CTO, Software Engineer, Research Scientist, Director of AI/ML

Related on AIssential

• More Prompts = Worse Code? — Complex or outdated prompts create technical debt, silently degrading LLM performance due to model-specific decay.
• AI Coding Tools: The Productivity Trap Most Companies Miss — AI coding tools accelerate development but introduce significant, hard-to-detect technical debt, especially in legacy systems.
• Beyond Problem Solving: UOJ-Bench for Evaluating Code Generation, Hacking, and Repair in Competitive Programming — LLMs can complement traditional online judges by identifying subtle code errors, but effective error detection remains computationally intensive.
• Another try at LLM workflow automation — Effective LLM automation requires extensive scaffolding and iterative tooling, not just smarter models.
• Structured-Prompt-Driven Development (SPDD) — SPDD transforms LLM assistance into a scalable, governable organizational capability by treating prompts as version-controlled artifacts.
• The TechBeat: Can LLMs Generate Quality Code? A 40,000-Line Experiment (1/6/2026) — LLMs generate sloppy code over time, requiring formal analysis and multi-model reviews for quality assurance.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.