一个模型就够了吗？MuCoS：一种用于语义代码搜索的多模型包围学习算法
Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search
摘要：
最近，深度学习方法已经成为代码搜索中的主流，因为它们在捕获代码片段和搜索查询之间的语义相关性方面做得更好，并且具有有前途的性能。然而，代码片段包含来自不同维度的信息，例如业务逻辑、特定算法和硬件通信，因此单个代码表示模块很难涵盖所有视角。另一方面，由于特定查询可能集中在一个或多个视角上，因此单个查询表示模块难以表示不同的用户意图。在本文中，我们提出了MuCoS，多模型集成学习架构的语义代码搜索。它结合了几个单独的学习器，每个学习器都强调代码段的特定视角。我们训练个人学习者在不同的数据集，其中包含不同的角度的代码信息，我们使用一个数据增强策略，以获得这些不同的数据集。然后，我们集成学习器来捕获代码片段的综合特征。
英文摘要：
Recently, deep learning methods have become mainstream in code search since they do better at capturing semantic correlations between code snippets and search queries and have promising performance. However, code snippets have diverse information from different dimensions, such as business logic, specific algorithm, and hardware communication, so it is hard for a single code representation module to cover all the perspectives. On the other hand, as a specific query may focus on one or several perspectives, it is difficult for a single query representation module to represent different user intents. In this paper, we propose MuCoS, a multi-model ensemble learning architecture for semantic code search. It combines several individual learners, each of which emphasizes a specific perspective of code snippets. We train the individual learners on different datasets which contain different perspectives of code information, and we use a data augmentation strategy to get these different datasets. Then we ensemble the learners to capture comprehensive features of code snippets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
