Code fragment recommendation method considering code statement sequence information

Abstract

The invention discloses a code fragment recommendation method considering code statement sequence information, which comprises the following steps: obtaining a current code context, carrying out formatting, structure information and declarated variable type extraction, and converting a cleaned code fragment into an LC sequence; calculating BWT similarity and variable type similarity of the code segments in the code database and the current code context, and obtaining a plurality of code segments with the highest similarity with the current code context as candidate sets; and reordering the candidate code segments according to the BWT similarity, variable similarity and structural similarity of the code segments in the candidate set and the current code context, and presenting a reordered list to the user. Compared with the prior art, the sequence information between the code statements is used, so that the similarity between the recommended code snippets and the query is higher, and auser can better use the recommended code snippets. And moreover, structural information is introduced in the sorting process, so that a better sorting effect can be achieved.

Description

technical field [0001] The invention relates to code recommendation, in particular to a method for recommending code fragments considering the sequence information of code statements. Background technique [0002] A code fragment refers to a set of code sequences, which can guide developers to quickly grasp the usage of a certain program interface or the implementation method of a certain programming task. In recent years, research in the direction of code recommendation has focused on how to use information retrieval or machine learning for code recommendation. The code features used in these studies are relatively single, and they all focus on the text information of the code. Taking ROSF as an example, in the data preparation stage, it first divides the Java project into multiple class files, and then divides the class files into multiple code fragments. Afterwards, for each code fragment, it uses topic models, code metrics, etc. to extract three features of code fragme...

AI Technical Summary

Problems solved by technology

Existing code recommendation technologies mostly use code text information to make relevant recommendations. Only using text information for code recommendation will cause the loss of the structural information and sequence information of the code itself, resulting in inaccurate recommendation results.

And the existing code recommendation technology does not fully consider the similarity between the final recommendation result and the current programming context, which will lead to poor final ranking effect

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