Unverified50% confidenceFactExact time
The study found that end-to-end delivered software did not grow proportionally with code output, code review is still done manually, and change requests initiated by reviewers grew almost linearly as agent adoption deepened.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/17/2026
First Seen
Valid until: 1/15/2027
Sources
Related Entities
Related Claims
UnverifiedIn traditional workflows, review workload grows roughly in sync with code output; but when AI agents greatly increase the speed of code output, the arrival rate of review requests may exceed human reviewers' processing capacity, creating queues and backlogs.75% similarUnverified大模型生成的代码在功能正确性上的通过率（如 HumanEval 基准测试）逐年提升但仍未达到100%，在并发、状态管理、异常处理等复杂场景中错误率显著上升71% similarVerifiedA higher acceptance rate means the draft model's guesses are more accurate and the large model needs to redo less work, which directly determines the actual speedup achieved by speculative decoding.70% similarUnverifiedThe main reason for the surge in time at high effort levels is not that code generation becomes slower, but that the model is allowed to repeatedly execute a generate-check-correct loop; what the effort level actually buys is the number of iterative tuning rounds rather than the sheer amount of code.70% similarUnverifiedMaking code differentiable is often a rigorous numerical sanity review, because gradient computation demands far higher numerical stability than purely forward evaluation.69% similar
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