This paper presents a novel approach to enhance the performance of binary
code comment quality classification models through the application of
Generative Artificial Intelligence (AI). By leveraging the OpenAI API, a
dataset comprising 1239 newly generated code-comment pairs, extracted from
various GitHub repositories and open-source projects, has been labelled as
"Useful" or "Not Useful", and integrated into the existing corpus of 9048 pairs
in the C programming language. Employing a cutting-edge Large Language Model
Architecture, the generated dataset demonstrates notable improvements in model
accuracy. Specifically, when incorporated into the Support Vector Machine (SVM)
model, a 6% increase in precision is observed, rising from 0.79 to 0.85.
Additionally, the Artificial Neural Network (ANN) model exhibits a 1.5%
increase in recall, climbing from 0.731 to 0.746. This paper sheds light on the
potential of Generative AI in augmenting code comment quality classification
models. The results affirm the effectiveness of this methodology, indicating
its applicability in broader contexts within software development and quality
assurance domains. The findings underscore the significance of integrating
generative techniques to advance the accuracy and efficacy of machine learning
models in practical software engineering scenarios.

通过应用生成式人工智能（AI）提升二进制代码注释质量分类模型性能，使用 OpenAI API 生成了包含 1239 个新生成的代码 - 注释对的数据集，该数据集从各种 GitHub 代码库和开源项目中提取，并为其标记为 “有用” 或 “无用”，并与现有的 9048 个 C 编程语言对称整合。当将生成的数据集集成到支持向量机（SVM）模型中时，精确度提高了 6％，从 0.79 提高到 0.85。此外，人工神经网络（ANN）模型的召回率增加了 1.5％，从 0.731 提高到 0.746。研究结果表明，生成式 AI 在增强代码注释质量分类模型中的潜力，验证了该方法的有效性，并显示出其在软件开发和质量保证领域中的更广泛应用性。研究结果强调了整合生成技术以提高机器学习模型在实际软件工程场景中的准确性和效果的重要性。

基于生成人工智能的软件元数据分类

Software Metadata Classification based on Generative Artificial  Intelligence

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。