This report focuses on enhancing a binary code comment quality classification
model by integrating generated code and comment pairs, to improve model
accuracy. The dataset comprises 9048 pairs of code and comments written in the
C programming language, each annotated as "Useful" or "Not Useful."
Additionally, code and comment pairs are generated using a Large Language Model
Architecture, and these generated pairs are labeled to indicate their utility.
The outcome of this effort consists of two classification models: one utilizing
the original dataset and another incorporating the augmented dataset with the
newly generated code comment pairs and labels.

通过整合生成的代码和注释对，提高二进制代码注释质量分类模型的准确性， 使用包含 9048 对用 C 语言编写的代码和注释的数据集，每个都标注为 “有用” 或 “无用”。除此之外，还使用大型语言模型架构生成代码和注释对，并标记以指示其效用。这一努力的成果包括两个分类模型：一个利用原始数据集，另一个整合了新生成的代码注释对和标签的增强数据集。

提升二进制代码注释质量分类：集成生成人工智能以提高准确性

Enhancing Binary Code Comment Quality Classification: Integrating  Generative AI for Improved Accuracy

Filter pruning is widely adopted to compress and accelerate the Convolutional
Neural Networks (CNNs), but most previous works ignore the relationship between
filters and channels in different layers. Processing each layer independently
fails to utilize the collaborative relationship across layers. In this paper,
we intuitively propose a novel pruning method by explicitly leveraging the
Filters Similarity in Consecutive Layers (FSCL). FSCL compresses models by
pruning filters whose corresponding features are more worthless in the model.
The extensive experiments demonstrate the effectiveness of FSCL, and it yields
remarkable improvement over state-of-the-art on accuracy, FLOPs and parameter
reduction on several benchmark models and datasets.

本研究介绍了一种新的滤波器修剪方法（FSCL）， 它在连续层之间明确利用滤波器之间的相似性来压缩模型，从而剪掉那些在模型中不太重要的特征， 并在多个基准模型和数据集上取得了显着的精度、FLOPs 和参数量减少。