Observational study of 20,574 sessions identifies seven misalignment forms where 90.5% cause effort/trust costs and 91.5% require explicit user correction, varying by interface and over time.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Hot fixes show urgency patterns with reduced collaboration and testing, differing from regular fixes, and human versus AI agents display over 10 distinct repair behaviors in large-scale GitHub data.
citing papers explorer
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How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
Observational study of 20,574 sessions identifies seven misalignment forms where 90.5% cause effort/trust costs and 91.5% require explicit user correction, varying by interface and over time.
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Hot Fixing in the Wild
Hot fixes show urgency patterns with reduced collaboration and testing, differing from regular fixes, and human versus AI agents display over 10 distinct repair behaviors in large-scale GitHub data.