Deep Learning (DL) frameworks play a critical role in advancing artificial
intelligence, and their rapid growth underscores the need for a comprehensive
understanding of software quality and maintainability. DL frameworks, like
other systems, are prone to code clones. Code clones refer to identical or
highly similar source code fragments within the same project or even across
different projects. Code cloning can have positive and negative implications
for software development, influencing maintenance, readability, and bug
propagation. In this paper, we aim to address the knowledge gap concerning the
evolutionary dimension of code clones in DL frameworks and the extent of code
reuse across these frameworks. We empirically analyze code clones in nine
popular DL frameworks, i.e., TensorFlow, Paddle, PyTorch, Aesara, Ray, MXNet,
Keras, Jax and BentoML, to investigate (1) the characteristics of the long-term
code cloning evolution over releases in each framework, (2) the short-term,
i.e., within-release, code cloning patterns and their influence on the
long-term trends, and (3) the file-level code clones within the DL frameworks.
Our findings reveal that DL frameworks adopt four distinct cloning trends and
that these trends present some common and distinct characteristics. For
instance, bug-fixing activities persistently happen in clones irrespective of
the clone evolutionary trend but occur more in the "Serpentine" trend.
Moreover, the within release level investigation demonstrates that short-term
code cloning practices impact long-term cloning trends. The cross-framework
code clone investigation reveals the presence of functional and architectural
adaptation file-level cross-framework code clones across the nine studied
frameworks. We provide insights that foster robust clone practices and
collaborative maintenance in the development of DL frameworks.

该研究分析了深度学习框架中的代码克隆现象的演化特征，包括长期演化趋势、短期克隆模式以及框架间的代码克隆。研究结果揭示了深度学习框架中存在四种不同的克隆演化趋势，并探讨了克隆对 bug 修复活动的影响。此外，研究还发现了不同框架之间存在功能和架构适应性的文件级别的代码克隆。研究结果为深度学习框架的可靠克隆实践和协同维护提供了启示。

揭示深度学习框架中的代码克隆动态

Unraveling Code Clone Dynamics in Deep Learning Frameworks

Developers introduce code clones to improve programming productivity. Many
existing studies have achieved impressive performance in monolingual code clone
detection. However, during software development, more and more developers write
semantically equivalent programs with different languages to support different
platforms and help developers translate projects from one language to another.
Considering that collecting cross-language parallel data, especially for
low-resource languages, is expensive and time-consuming, how designing an
effective cross-language model that does not rely on any parallel data is a
significant problem. In this paper, we propose a novel method named ZC3 for
Zero-shot Cross-language Code Clone detection. ZC3 designs the contrastive
snippet prediction to form an isomorphic representation space among different
programming languages. Based on this, ZC3 exploits domain-aware learning and
cycle consistency learning to further constrain the model to generate
representations that are aligned among different languages meanwhile are
diacritical for different types of clones. To evaluate our approach, we conduct
extensive experiments on four representative cross-language clone detection
datasets. Experimental results show that ZC3 outperforms the state-of-the-art
baselines by 67.12%, 51.39%, 14.85%, and 53.01% on the MAP score, respectively.
We further investigate the representational distribution of different languages
and discuss the effectiveness of our method.

本文提出了一种名为 ZC3 的新方法，用于零射击交叉语言代码克隆检测，通过对比片段预测、领域感知学习和循环一致性学习来生成在不同编程语言之间对齐且区分不同克隆类型的表示，实验证明 ZC3 相对于现有基准模型在 MAP 评分上分别提高了 67.12％，51.39％，14.85％和 53.01％。

ZC3：零样本跨语言代码克隆检测

ZC3: Zero-Shot Cross-Language Code Clone Detection

Code clones are semantically similar code fragments pairs that are
syntactically similar or different. Detection of code clones can help to reduce
the cost of software maintenance and prevent bugs. Numerous approaches of
detecting code clones have been proposed previously, but most of them focus on
detecting syntactic clones and do not work well on semantic clones with
different syntactic features. To detect semantic clones, researchers have tried
to adopt deep learning for code clone detection to automatically learn latent
semantic features from data. Especially, to leverage grammar information,
several approaches used abstract syntax trees (AST) as input and achieved
significant progress on code clone benchmarks in various programming languages.
However, these AST-based approaches still can not fully leverage the structural
information of code fragments, especially semantic information such as control
flow and data flow. To leverage control and data flow information, in this
paper, we build a graph representation of programs called flow-augmented
abstract syntax tree (FA-AST). We construct FA-AST by augmenting original ASTs
with explicit control and data flow edges. Then we apply two different types of
graph neural networks (GNN) on FA-AST to measure the similarity of code pairs.
As far as we have concerned, we are the first to apply graph neural networks on
the domain of code clone detection.
We apply our FA-AST and graph neural networks on two Java datasets: Google
Code Jam and BigCloneBench. Our approach outperforms the state-of-the-art
approaches on both Google Code Jam and BigCloneBench tasks.

本文提出了一种基于图神经网络 (GNN) 的程序控制流和数据流表示方法 FA-AST (flow-augmented abstract syntax tree)，并将其应用于代码克隆检测，相比基于抽象语法树 (AST) 的方法，在 Google Code Jam 和 BigCloneBench 数据集上均取得了更好的效果。