An Empirical Study of Speculative Decoding on Software Engineering Tasks
New study shows speculative decoding cuts latency for code generation and repair tasks.
Researchers conducted the first systematic empirical study to evaluate the effectiveness of speculative decoding (SD) on software engineering tasks, including code generation, editing, and repair. They found SD shows clear potential for accelerating inference, particularly for smaller models that achieve higher speedups than larger ones. Model-based approaches are well-suited for code generation, while model-free methods are better adapted to repository-level repair and editing scenarios. The higher predictability of SE tasks allows for more aggressive hyperparameters compared to natural language tasks.
- Speculative decoding achieves up to 2x speedup on software engineering tasks like code generation, editing, and repair.
- Model-based SD (e.g., Medusa, Eagle) works best for code generation; model-free methods excel at repository-level repairs.
- SE code's predictability allows more aggressive hyperparameters than natural language, boosting SD efficiency.
Why It Matters
This study provides practical guidelines for developers to accelerate LLM-driven coding tools without sacrificing quality.