基于Stack Overflow网站获取Web API知识的系统和方法 (2023)
本发明提供了一种基于Stack Overflow网站获取Web API知识的系统和方法，从Stack Overflow网站公开的数据转储中下载数据文件，筛选最相关数据标识为正样本，其余数据标识为无标记样本，利用半监督学习从无标记样本中筛选出正样本；将问题分为不同类别，对问题主体进行分句切分，使用深度学习模型对切分后的句子进行分类，根据分类结果，统计每个问题在不同类别上的句子数量，形成训练集进行训练预测模型，通过预测模型预测问题类别，得到每个问题的所属类别；针对每个问题的所属类别和每个问题的发布时间，采用时间序列分析预测对Web API的性能进行衡量预测，形成关于Web API见解。 The invention provides a system and a method for acquiring Web API knowledge based on a Stack Overflow website. The method includes: downloading a data file from data dump disclosed by a Stack Overflow website, screening most relevant data identifiers as positive samples, identifying other data identifiers as unmarked samples, and screening out the positive samples from the unmarked samples by utilizing semi-supervised learning; classifying questions into different categories, performing sentence segmentation on question subjects, classifying segmented sentences by using a deep learning model,counting the number of sentences of each question on different categories according to a classification result, forming a trainin