CIAO uses LLMs to generate standards-based system-level software architecture documentation from code repositories, with a developer study showing it is generally valuable, comprehensible, and accurate.
Technical debt: From metaphor to theory and practice.IEEE Software.2012;29(6):18–21
4 Pith papers cite this work, alongside 691 external citations. Polarity classification is still indexing.
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Analysis of 252 bug fixes in an LLM-powered multi-market web app found 44% escaped through four seams invisible to component unit tests, motivating a four-seam verification framework.
A practitioner study with 11 participants produces a six-family taxonomy of stakeholder criteria for technical debt decisions, distinguishing acquisition from repayment functions.
A systematic literature review summarizing the shift in SATD detection from heuristic keyword methods to ML, DL, and Transformer models, along with performance trends and open challenges like dataset heterogeneity.
citing papers explorer
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CIAO - Code In Architecture Out - Automated Software Architecture Documentation with Large Language Models
CIAO uses LLMs to generate standards-based system-level software architecture documentation from code repositories, with a developer study showing it is generally valuable, comprehensible, and accurate.
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All Green, Still Broken: Real-Flow Verification Lessons from an LLM-Integrated, Multi-Market Web Application
Analysis of 252 bug fixes in an LLM-powered multi-market web app found 44% escaped through four seams invisible to component unit tests, motivating a four-seam verification framework.
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Stakeholder Criteria in Technical Debt Decision-Making: A Practitioner-Informed Taxonomy
A practitioner study with 11 participants produces a six-family taxonomy of stakeholder criteria for technical debt decisions, distinguishing acquisition from repayment functions.
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Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
A systematic literature review summarizing the shift in SATD detection from heuristic keyword methods to ML, DL, and Transformer models, along with performance trends and open challenges like dataset heterogeneity.