This paper introduces a novel code-to-code search technique that enhances the
performance of Large Language Models (LLMs) by including both static and
dynamic features as well as utilizing both similar and dissimilar examples
during training. We present the first-ever code search method that encodes
dynamic runtime information during training without the need to execute either
the corpus under search or the search query at inference time and the first
code search technique that trains on both positive and negative reference
samples. To validate the efficacy of our approach, we perform a set of studies
demonstrating the capability of enhanced LLMs to perform cross-language
code-to-code search.
Our evaluation demonstrates that the effectiveness of our approach is
consistent across various model architectures and programming languages. We
outperform the state-of-the-art cross-language search tool by up to 44.7\%.
Moreover, our ablation studies reveal that even a single positive and negative
reference sample in the training process results in substantial performance
improvements demonstrating both similar and dissimilar references are important
parts of code search. Importantly, we show that enhanced well-crafted,
fine-tuned models consistently outperform enhanced larger modern LLMs without
fine tuning, even when enhancing the largest available LLMs highlighting the
importance for open-sourced models.
To ensure the reproducibility and extensibility of our research, we present
an open-sourced implementation of our tool and training procedures called
Cosco.

本文介绍了一种新的代码到代码搜索技术，通过包括静态和动态特征以及在训练过程中利用相似和不同的示例来提高大型语言模型的性能。我们展示了第一个能够在训练期间编码动态运行时信息的代码搜索方法，在推断时间无需执行搜索语料库或搜索查询，同时还是第一个训练正负参考样本的代码搜索技术。我们通过一系列研究验证了我们方法的有效性，并展示了增强 LLMs 执行跨语言代码到代码搜索的能力。我们的评估表明，我们的方法的效果在各种模型架构和编程语言中都是一致的。此外，我们的消融研究表明，即使在训练过程中只有一个正面和负面参考样本，也会产生相当大的性能提高，这证明了相似和不同的参考是代码搜索的重要部分。重要的是，我们展示了精心制作的、经过调整的模型在性能上始终优于未调整的更大的现代 LLMs，即使是在增强最大可用 LLMs 时也是如此，这凸显了开源模型的重要性。为了确保我们研究的可重复性和可扩展性，我们介绍了一个名为 Cosco 的开源实现和培训过程