Transformer-based code programming language classification method

Abstract

The invention provides a Transformer-based code programming language classification method, which comprises the following steps: (1) collecting question and answer posts from Stack Overflow as a data set, and carrying out data preprocessing on data in the original data set; (2) carrying out word embedding on the data subjected to word segmentation by using the BPE to convert words into vectors; (3) on the basis of the constructed data set, performing fine tuning on the RoBERTa model, inputting a generated word vector into the RoBERTa model, performing code semantic learning through a double-layer Transform encoder, and generating a semantic representation vector Xsematic; and (4) mapping the semantic vector Xsemination to a programming language category label through a linear layer, and obtaining a corresponding programming language through a Softmax algorithm. The method has the beneficial effects that the code type can be quickly identified according to the code snippets so as to play a role in assisting developers to quickly find the solution on the question and answer website.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method for classifying code programming languages ​​based on Transformer. Background technique [0002] In the software development cycle, different development tasks usually use different programming languages ​​(such as Java, Python, C#, C language, etc.). In most cases, different programming language types of problems require different solutions. In the process of software development, programmers often encounter various problems, and posting on question-and-answer websites to seek solutions has become the mainstream solution. Therefore, when a developer asks a question on a question-and-answer website (such as Stack Overflow), the website needs to mark the type of the language to quickly find the corresponding solution. Stack Overflow relies on the language tag of the source code in the post to match users who can provide answers. . However, new users or novice develop...

AI Technical Summary

Problems solved by technology

[0003] Existing works have established classification models through machine learning methods, such as naive Bayesian classifiers, or random forest classifiers, but traditional machine learning-based classification methods have certain bottlenecks in performance, and the classification effect is not good

Compared with traditional machine learning methods, the effect of a few classification methods using deep learning models (CNN, RNN) has improved but the effect is still not good

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