Research on software classification based on LSTM and CNN

Yuhang Chen; Shizhou Wang

doi:10.1117/12.3034861

11 July 2024 Research on software classification based on LSTM and CNN

Yuhang Chen, Shizhou Wang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100V (2024) https://doi.org/10.1117/12.3034861
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Many programmers will look for available software in the code base, but there are lots of software without classification labels in the code base. If the software is not classified correctly, it will prevent programmers from retrieving the libraries they need, affecting the development process. Based on the above background, this article selects the Maven code warehouse as the research object, using the convolutional neural network model (CNN) and long short-term memory network model (LSTM) in deep learning. This paper uses software code, text, and dependencies as input to the model. This paper aims to assess the accuracy of software classification. At the same time, the parameter configuration of the model is continuously optimized during the training process to find the parameters that enable the model to achieve optimal results. The experimental results show our model can help developers find suitable software and can be useful in classifying software systems in open-source repositories.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuhang Chen and Shizhou Wang "Research on software classification based on LSTM and CNN", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100V (11 July 2024); https://doi.org/10.1117/12.3034861

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