Recently, large code generation models trained in a self-supervised manner on
extensive unlabeled programming language data have achieved remarkable success.
While these models acquire vast amounts of code knowledge, they perform poorly
on code understanding tasks, such as code search and clone detection, as they
are specifically trained for generation. Pre-training a larger encoder-only
architecture model from scratch on massive code data can improve understanding
performance. However, this approach is costly and time-consuming, making it
suboptimal. In this paper, we pioneer the transfer of knowledge from
pre-trained code generation models to code understanding tasks, significantly
reducing training costs. We examine effective strategies for enabling
decoder-only models to acquire robust code representations. Furthermore, we
introduce CL4D, a contrastive learning method designed to enhance the
representation capabilities of decoder-only models. Comprehensive experiments
demonstrate that our approach achieves state-of-the-art performance in
understanding tasks such as code search and clone detection. Our analysis shows
that our method effectively reduces the distance between semantically identical
samples in the representation space. These findings suggest the potential for
unifying code understanding and generation tasks using a decoder-only
structured model.

大型代码生成模型在自监督学习的基础上取得了显著的成功，但它们在代码理解任务上表现不佳。本文将知识从预训练的代码生成模型转移到代码理解任务中，显著降低了训练成本，实现了理解任务的最先进性能。

探索预训练代码生成模型的代码理解能力

Toward Exploring the Code Understanding Capabilities of Pre-trained Code  Generation Models

In recent times, a plethora of Large Code Generation Models (LCGMs) have been
proposed, showcasing significant potential in assisting developers with complex
programming tasks. Benchmarking LCGMs necessitates the creation of a set of
diverse programming problems, and each problem comprises the prompt (including
the task description), canonical solution, and test inputs. The existing
methods for constructing such a problem set can be categorized into two main
types: manual methods and perturbation-based methods. However, manual methods
demand high effort and lack scalability, while also risking data integrity due
to LCGMs' potentially contaminated data collection, and perturbation-based
approaches mainly generate semantically homogeneous problems with the same
canonical solutions and introduce typos that can be easily auto-corrected by
IDE, making them ineffective and unrealistic. In this work, we propose the idea
of programming problem merging (PPM) and provide two implementation of this
idea, we utilize our tool on two widely-used datasets and compare it against
nine baseline methods using eight code generation models. The results
demonstrate the effectiveness of our tool in generating more challenging,
diverse, and natural programming problems, comparing to the baselines.

我们提出了编程问题合并（PPM）的概念，并提供了两种实现方法，我们使用我们的工具在两个广泛使用的数据集上，并使用八个代码生成模型将其与九种基准方法进行了比较，结果表明，与基准相比，我们的工具在生成更具挑战性、多样化和自然的编程问题方面更具有效性。