2021/04/16 by Abdullah Al Ishtiaq, Ishtiaq, Abdullah Al, Masum Hasan +13 · 1 voice · 6 citations
Computer Science · #Artificial intelligence #Bottleneck #Code (set theory) #Computation and Language (cs.CL) #Computer science #Embedding #FOS: Computer and information sciences #Information retrieval #Language model #Leverage (statistics) #Natural language #Natural language processing #Programming language #Scope (computer science) #Simple (philosophy) #Software Engineering (cs.SE) #Software Engineering Research #Software Testing and Debugging Techniques #Topic Modeling #cs.CL #cs.SE
paper · pdf · doi:10.48550/arxiv.2104.08017
published in arXiv (Cornell University) (Cornell University) · Submitted to ICANN2021
arxiv created 2021/04/16 · openalex publication_date 2021/04/16 · arxiv published 2021/04/16 · arxiv updated 2021/04/19 · openalex created_date 2021/04/26 · openalex updated_date 2026/07/28
Millions of repetitive code snippets are submitted to code repositories every day. To search from these large codebases using simple natural language queries would allow programmers to ideate, prototype, and develop easier and faster. Although the existing methods have shown good performance in searching codes when the natural language description contains keywords from the code, they are still far behind in searching codes based on the semantic meaning of the natural language query and semantic structure of the code. In recent years, both natural language and programming language research communities have created techniques to embed them in vector spaces. In this work, we leverage the efficacy of these embedding models using a simple, lightweight 2-layer neural network in the task of semantic code search. We show that our model learns the inherent relationship between the embedding spaces and further probes into the scope of improvement by empirically analyzing the embedding methods. In this analysis, we show that the quality of the code embedding model is the bottleneck for our model's performance, and discuss future directions of study in this area.