Research on software classification based on the fusion of code and descriptive text
Third-party software systems play a significant role in modern software development.Software developers build software based on requirements by retrieving appropriate dependency libraries from third-party software repositories,effectively avoiding repetitive wheel-building operations and thus speeding up the development process.However,retrieving third-party dependency libraries can be challenging.Typically,third-party software repositories provide preset tags(categories)for software developers to search.However,when a software's preset tags are incorrectly labeled,software developers are unable to find the libraries required,and this inevitably affects the development process.This study proposes a software clustering model to address the aforementioned challenges.The model combines method vectors,method importance,and text vectors to categorize unknown categories of software into known categories.In addition,because no publicly available dataset exists for this problem,we built a dataset and made it publicly available.This clustering model was tested on a self-built dataset comprising 30 categories and software systems from the Maven repository.The accuracy of the prediction category was 70%for one candidate(top-1)and 90%for three candidates(top-3).The experimental results show that our model can help software developers find suitable software,can be useful for classifying software systems in open-source repositories,and can assist software developers in quickly locating third-party libraries.
万方数据
维普
