← 研踪 PaperTrailarXiv:2611.15310 · v1
Technical Debt Risks in AI-Assisted Software Development: Insights from Practitioners
首次提交 2026-11-11 18:45
版本更新 2026-11-11 18:45
雷达观测 2026-11-13 11:11
摘要
AI coding assistants increasingly support software development, raising concerns about technical debt associated with AI-generated code and development practices. We investigate these concerns through a sequential mixed-methods study involving 137 survey respondents and 20 follow-up interviews with software professionals in Bangladesh. Drawing on ten established technical debt dimensions, we examine perceived risks, contributing circumstances, maintenance consequences, validation practices, and developer understanding. Respondents expressed the strongest concerns about People Debt (73.0\%), Management Debt (70.1\%), and Architecture Debt (68.6\%), suggesting that risks extend beyond code quality. Participants associated ambiguous requirements, insufficient project context, and unrequested AI-generated changes with architectural inconsistencies, rework, and maintenance difficulties. Their experiences revealed contrasting outcomes: AI assistance supported refactoring, maintainability improvements, and testing, but also introduced rework and context misalignment. Although 89.8\% reported regularly verifying AI-generated code, participants expressed concerns about AI dependence, reduced code understanding, and review effort. They also emphasized the need for stronger architectural awareness, requirements clarification, and validation support in coding assistants. Our findings suggest that managing technical debt risks requires attention to project context, implementation decisions, and developer understanding alongside generated code quality.
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v1 · 2026-11-11 18:45 · 最新观测版本
Technical Debt Risks in AI-Assisted Software Development: Insights from Practitioners
AI coding assistants increasingly support software development, raising concerns about technical debt associated with AI-generated code and development practices. We investigate these concerns through a sequential mixed-methods study involving 137 survey respondents and 20 follow-up interviews with software professionals in Bangladesh. Drawing on ten established technical debt dimensions, we examine perceived risks, contributing circumstances, maintenance consequences, validation practices, and developer understanding. Respondents expressed the strongest concerns about People Debt (73.0\%), Management Debt (70.1\%), and Architecture Debt (68.6\%), suggesting that risks extend beyond code quality. Participants associated ambiguous requirements, insufficient project context, and unrequested AI-generated changes with architectural inconsistencies, rework, and maintenance difficulties. Their experiences revealed contrasting outcomes: AI assistance supported refactoring, maintainability improvements, and testing, but also introduced rework and context misalignment. Although 89.8\% reported regularly verifying AI-generated code, participants expressed concerns about AI dependence, reduced code understanding, and review effort. They also emphasized the need for stronger architectural awareness, requirements clarification, and validation support in coding assistants. Our findings suggest that managing technical debt risks requires attention to project context, implementation decisions, and developer understanding alongside generated code quality.
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