The study of code example recommendation has been conducted extensively in the past and recently in order to assist developers in their software development tasks. This is because developers often spend significant time searching for relevant code examples on the internet, utilizing open-source projects and informal documentation. For finding useful code examples, informal documentation, such as Stack Overflow discussions and forums, can be invaluable. We have focused our research on Stack Overflow, which is a popular resource for discussing different topics among software developers. For increasing the quality of the recommended code examples, we have collected and recommended the best code examples in the Java programming language. We have utilized BERT in our approach, which is a Large Language Model (LLM) for text representation that can effectively extract semantic information from textual data. Our first step involved using BERT to convert code examples into numerical vectors. Subsequently, we applied LSH to identify Approximate Nearest Neighbors (ANN). Our research involved the implementation of two variants of this approach, namely the Random Hyperplane-based LSH and the Query-Aware LSH. Our study compared two algorithms using four parameters: HitRate, Mean Reciprocal Rank (MRR), Average Execution Time, and Relevance. The results of our analysis revealed that the Query- Aware (QA) approach outperformed the Random Hyperplane-based (RH) approach in terms of HitRate. Specifically, the QA approach achieved a HitRate improvement of 20% to 35% for query pairs compared to the RH approach. Creating hashing tables and assigning data samples to buckets using the QA approach is at least four times faster than the RH approach. The QA approach returns code examples within milliseconds, while it takes several seconds (sec) for the RH approach to recommend code examples.

利用BERT模型将Stack Overflow上的Java编程语言代码样例转换为数字向量，并应用LSH算法识别近似最近邻，对比了Random Hyperplane-based LSH 和 Query-Aware LSH两个算法，发现Query-Aware LSH算法的HitRate提高了20％到35％，而且至少比Random Hyperplane-based LSH算法快4倍，几乎可以即时返回代码样例。

使用BERT和查询感知LSH改进非正式文档中的代码示例推荐：一项比较研究