Code recommendation method based on long short-term memory (LSTM) network

A technology of long short-term memory and recommendation method, which is applied in the fields of instrumentation, computing, electrical and digital data processing, etc., and can solve the problems of low recommendation efficiency and inability to consider time series information.

Inactive Publication Date: 2017-12-22
WUHAN UNIV
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Problems solved by technology

[0032] In the code recommendation system, the existing code recommendation algorithm cannot consider timing information and low recommendation efficiency, etc., the present invention proposes a code recommendation method based on long-term short-term memory network

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  • Code recommendation method based on long short-term memory (LSTM) network
  • Code recommendation method based on long short-term memory (LSTM) network
  • Code recommendation method based on long short-term memory (LSTM) network

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Embodiment Construction

[0070] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0071] The process of the code recommendation method based on the long short-term memory network provided by the present invention is shown in the appendix figure 1 , all steps can be automatically run by those skilled in the art using computer software technology. The specific implementation process of the embodiment is as follows:

[0072] Step 1. In order to make the source code library have high credibility and practicability, at least 10,000 Java open source software codes are crawled from the GitHub website through web crawlers, and the number of updat...

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Abstract

The invention relates to a code recommendation method based on a long short-term memory (LSTM) network. For the problem that low recommendation accuracy rates, low recommendation efficiency and the like are ubiquitous in existing code recommendation technologies, the method firstly extracts source code to form an API sequence, utilizing the long short-term memory network to build a code recommendation model to learn relationships between API calls, and then carries out code recommendation. A dropout technology is used to prevent model overfitting. At the same time, using a ReLu function to instead a traditional saturation function is provided, the gradient vanishing problem is solved, a model convergence speed is accelerated, model performance is improved, and advantages of the neural network are fully exerted. The technical scheme of the invention has the characteristics of simpleness and quickness, and can better improve an accuracy rate and recommendation efficiency of code recommendation.

Description

technical field [0001] The invention belongs to the field of code recommendation, in particular to a code recommendation method based on a long short-term memory network. Background technique [0002] (1) Code recommendation system [0003] Developers often use mature software frameworks and class libraries for development to improve the efficiency and quality of software development. Therefore, developers often need to know how to reuse existing class libraries or frameworks by calling corresponding APIs. But learning unfamiliar library functions or APIs in frameworks is a big hurdle in the software development process. On the one hand, in recent years, the number of newly added APIs in various mature software frameworks is very large, making developers need to spend more time understanding the APIs in these software frameworks. On the other hand, many factors such as insufficient or inaccurate API code samples, incomplete or incorrect API annotation documents, and the c...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535
Inventor 余啸殷晓飞刘进伍蔓姜加明崔晓晖
Owner WUHAN UNIV
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