Accelerating Code Search with Deep Hashing and Code Classification

Abstract

Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods on 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 efficient code search without sacrificing too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method on 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.

Deep code search efficiency based on clustering

“…Without evaluation metrics, it is impossible to understand the efficiency of code search models, therefore, extending evaluation metrics is crucial for code search research. [20][21][22][23]…”