SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion
This addresses the challenge of efficient and accurate code completion for developers working in complex software projects, representing an incremental improvement over existing retrieval-augmented methods.
The authors tackled the problem of low latency and high-quality code generation in realistic software repositories by introducing SpecAgent, which proactively constructs speculative context during indexing to anticipate future edits, achieving absolute gains of 9-11% over baselines and reducing inference latency.
Large Language Models (LLMs) excel at code-related tasks but often struggle in realistic software repositories, where project-specific APIs and cross-file dependencies are crucial. Retrieval-augmented methods mitigate this by injecting repository context at inference time. The low inference-time latency budget affects either retrieval quality or the added latency adversely impacts user experience. We address this limitation with SpecAgent, an agent that improves both latency and code-generation quality by proactively exploring repository files during indexing and constructing speculative context that anticipates future edits in each file. This indexing-time asynchrony allows thorough context computation, masking latency, and the speculative nature of the context improves code-generation quality. Additionally, we identify the problem of future context leakage in existing benchmarks, which can inflate reported performance. To address this, we construct a synthetic, leakage-free benchmark that enables a more realistic evaluation of our agent against baselines. Experiments show that SpecAgent consistently achieves absolute gains of 9-11% (48-58% relative) compared to the best-performing baselines, while significantly reducing inference latency.