Accelerating Code Search with Deep Hashing and Code Classification
This addresses efficiency issues in code search for developers, though it is incremental as it builds on existing models.
The paper tackled the problem of inefficient retrieval in deep learning-based code search by proposing CoSHC, a method using deep hashing and code classification, which saved over 90% of retrieval time while preserving at least 99% accuracy.
Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods of code search have shown promising results. However, previous methods focus on retrieval accuracy but lacked attention to the efficiency of the retrieval process. We propose a novel method CoSHC to accelerate code search with deep hashing and code classification, aiming to perform an efficient code search without sacrificing too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method to five code search models. Extensive experimental results indicate that compared with previous code search baselines, CoSHC can save more than 90% of retrieval time meanwhile preserving at least 99% of retrieval accuracy.