Abstract: When a developer is required to implement a function,but not knowing how to implement this function using a specific programming language,he/she usually needs to perform code query using natural language.It is time-consuming and labor-intensive to perform code query while programming.There have been bunch of code query tools proposed over the past years to assist developers,while most of the approaches require complex inputs or have low precision.We propose a new code query approach called CodeSearcher based on natural language description.Relying on the 〈natural language description,code snippet〉 data pairs extracted from Stack OverFlow,which is a software development related Q&A website,we design a neural network model and the corresponding training method to map “natural language description” and “code snippets” to the same vector space.CodeSearcher is different from the conventional code query systems.On the one hand,it accepts all kinds of user-provided code bases for searching,because the system only relies on the source codes without depending on the comments or description of the source codes;on the other hand,it no longer limits the form of code query process to “entering the natural language description and feeding back the code snippets”,but extends a code Q&A section,helping the users pick the appropriate code snippet by the characteristic key words,so that developers do not have to read all returned code snippets in detail.The experimental results show that CodeSearcher has high precision compared with the baseline.

Key words: Code query, Natural language processing, Stack OverFlow