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Code search with natural language helps us reuse existing code snippets.Thanks to the Transformer-based pretraining models, the performance of codesearch has been improved significantly. However, due to the quadraticcomplexity of multi-head self-attention, there is a limit on the input tokenlength. For efficient training on standard GPUs like V100, existing pretrainedcode models, including GraphCodeBERT, CodeBERT, RoBERTa (code), take the first256 tokens by default, which makes them unable to represent the completeinformation of long code that is greater than 256 tokens. To tackle the longcode problem, we propose a new baseline SEA (Split, Encode and Aggregate),which splits long code into code blocks, encodes these blocks into embeddings,and aggregates them to obtain a comprehensive long code representation. WithSEA, we could directly use Transformer-based pretraining models to model longcode without changing their internal structure and re-pretraining. We alsocompare SEA with sparse Trasnformer methods. With GraphCodeBERT as the encoder,SEA achieves an overall mean reciprocal ranking score of 0.785, which is 10.1%higher than GraphCodeBERT on the CodeSearchNet benchmark, justifying SEA as astrong baseline for long code search. Our source code and experimental data areavailable at: https://github.com/fly-dragon211/SEA.

Tackling Long Code Search with Splitting, Encoding, and Aggregating