From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion
arXiv:2604.01849v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated exceptional proficiency in code completion, they typically adhere to a Hard Completion (HC) paradigm, compelling the generation of fully concrete code even amidst insufficient context. Our analysis of 3 million real-world interactions exposes the limitations of this strategy: 61% of the generated suggestions were either edited after acceptance or rejected despite exhibiting over 80% similarity to the user's subsequent code, suggesting that models frequently make erroneous predictions at spe
The proliferation and increasing reliance on LLMs for code generation necessitate a re-evaluation of their fundamental operating paradigms to improve utility and reduce developer friction. This paper offers a cost-theoretic framework for uncertainty-aware code completion.
Improving the accuracy and relevance of AI-powered code completion directly impacts developer productivity, software quality, and the broader adoption of AI in software engineering. This insight impacts the efficacy of AI agents.
Current 'Hard Completion' models, which often make erroneous concrete predictions, are shown to be inefficient; a new 'Uncertainty-Aware Code Completion' paradigm could emerge where models strategically use placeholders.
- · AI software developers
- · Software engineers (users)
- · Cloud infrastructure providers
- · Generative AI models with poor uncertainty handling
- · Developers reliant on current 'Hard Completion' paradigms
AI models for code generation become more nuanced, offering placeholders instead of outright incorrect code.
Developer workflows become more efficient as less time is spent correcting or rejecting AI-generated code.
The definition and expectations of 'code completion' evolve, pushing AI systems toward more intelligent, context-aware assistance rather than blind generation.
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Read at arXiv cs.CL