Enterprise AI Analysis
Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source
Executive Impact: Key Findings
This analysis investigates the long-term viability of AI-generated code in open-source projects. Contrary to the 'disposable code' hypothesis, AI-authored code survives significantly longer, with a 15.8 percentage-point lower modification rate. However, modification profiles differ, with AI code showing higher corrective rates and human code higher adaptive rates. Predictability of *when* modifications occur remains challenging, while *which* lines are modification-prone is more tractable.
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AI Code Survival Advantage
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AI Corrective Rate
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Human Adaptive Rate
Deep Analysis & Enterprise Applications
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Survival analysis demonstrates that AI-generated code survives significantly longer than human-authored code, challenging the 'disposable code' narrative. This analysis tracks code units from their birth (PR merge) through matched observation windows, revealing a 15.8 percentage-point lower modification rate for AI-authored code (Hazard Ratio = 0.842, p < 0.001) at the line level. This suggests that AI-generated code is not as fragile as commonly assumed, raising questions about maintenance paradigms. The log-rank test confirms statistical significance of these survival differences.
15.8%
Lower Modification Rate for AI Code (Line Level)
| Aspect | AI-Authored Code | Human-Authored Code |
|---|---|---|
| Modification Rate (Line-Level) | 53.9% | 69.3% |
| Survival Duration (Median) | 118.4 days | 120.5 days |
| Key Survival Factors |
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While AI code survives longer, its modification profile differs from human code. AI-authored code shows higher corrective (bug-fix) and preventive rates, while human-authored code exhibits higher adaptive rates. This suggests that AI-generated code may require more immediate bug fixes or security patches upon modification, whereas human code is more often adapted to environmental changes (e.g., dependency updates). The effect sizes are small (Cramér's V = 0.116), indicating that authorship explains a modest portion of variance, with per-agent variation often exceeding the agent-human gap.
+3.3pp
Higher Corrective Rate for AI Code
-5.1pp
Lower Adaptive Rate for AI Code
Enterprise Process Flow
AI Generates Code
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Code Merged
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Modification Occurs
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Intent Classified (Corrective, Adaptive, Perfective, Preventive, Other)
Predicting the fate of AI-generated code at birth presents asymmetric challenges. Localizing *which* lines are modification-prone is tractable (AUC-ROC = 0.671), with domain-specific textual features providing reliable signals. However, predicting *when* modifications will occur remains challenging (Macro F1 = 0.285), as temporal dynamics are largely driven by external organizational factors not captured by static analysis, such as bug surface, priority shifts, and maintainer availability. This implies that while code content offers some predictive power for 'where', 'when' is much harder to forecast.
0.671
AUC-ROC for Localizing Modification-Prone Lines
0.285
Macro F1 for Predicting Modification Timing (Challenging)
Impact on Code Review Workflows
By pinpointing modification-prone lines, AI models can significantly reduce the inspection scope from entire files to 13-30% of lines, enabling focused and efficient code reviews. This allows human developers to prioritize critical sections, leading to better resource allocation and potentially preventing future issues before they manifest. However, the difficulty in predicting when these modifications will occur highlights the need for dynamic, post-deployment signals like production error logs or CI failures to provide more timely insights.
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