Our research investigates the recommendation of code examples to aid software developers, a practice that saves developers significant time by providing ready-to-use code snippets. The focus of our study is Stack Overflow, a commonly used resource for coding discussions and solutions, particularly in the context of the Java programming language. We applied BERT, a powerful Large Language Model (LLM) that enables us to transform code examples into numerical vectors by extracting their semantic information. Once these numerical representations are prepared, we identify Approximate Nearest Neighbors (ANN) using Locality-Sensitive Hashing (LSH). Our research employed two variants of LSH: Random Hyperplane-based LSH and Query-Aware LSH. We rigorously compared these two approaches across four parameters: HitRate, Mean Reciprocal Rank (MRR), Average Execution Time, and Relevance. Our study revealed that the Query-Aware (QA) approach showed superior performance over the Random Hyperplane-based (RH) method. Specifically, it exhibited a notable improvement of 20% to 35% in HitRate for query pairs compared to the RH approach. Furthermore, the QA approach proved significantly more time-efficient, with its speed in creating hashing tables and assigning data samples to buckets being at least four times faster. It can return code examples within milliseconds, whereas the RH approach typically requires several seconds to recommend code examples. Due to the superior performance of the QA approach, we tested it against PostFinder and FaCoY, the state-of-the-art baselines. Our QA method showed comparable efficiency proving its potential for effective code recommendation.

翻译：本研究探讨了向软件开发人员推荐代码示例的方法，这一实践通过提供即用型代码片段为开发人员节省大量时间。我们聚焦于Stack Overflow这一常用于编码讨论和解决方案的资源平台，特别是针对Java编程语言的场景。我们应用了BERT——一种强大的大型语言模型（LLM），能够通过提取代码示例的语义信息将其转化为数值向量。在生成这些数值表示后，我们利用局部敏感哈希（LSH）识别近似最近邻（ANN）。本研究采用了两种LSH变体：基于随机超平面的LSH和查询感知LSH。我们从四个参数（命中率、平均倒数排名（MRR）、平均执行时间和相关性）对这两种方法进行了严格比较。研究结果显示，查询感知（QA）方法在性能上优于基于随机超平面（RH）的方法。具体而言，在查询对的命中率方面，QA方法比RH方法提升了20%至35%。此外，QA方法在时间效率上显著更优，其构建哈希表及将数据样本分配到桶中的速度至少快四倍，可在毫秒级返回代码示例，而RH方法通常需要数秒才能推荐代码示例。基于QA方法的优越性能，我们将其与当前最先进的基线方法PostFinder和FaCoY进行了对比测试。我们的QA方法展现出相当的效率，证明了其在有效代码推荐中的潜力。