GITG：面向Gitee平台的issue标题自动生成方法

杨君; 刘诗凡; 陈翔; 崔展齐

doi:10.12263/DZXB.20240434

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GITG：面向Gitee平台的issue标题自动生成方法

学术论文 | 更新时间：2025-08-18

- GITG：面向Gitee平台的issue标题自动生成方法
- GITG: Automatic Issue Title Generation Method for Gitee Platform
- 电子学报 2025年53卷第5期页码：1559-1570
- 作者机构：
  
  1.北京信息科技大学计算机学院，北京 100192
  2.南通大学信息科学技术学院，江苏南通 226019
- 作者简介：
  
  [ "杨君男，2000年10月生，北京人.现为北京信息科技大学硕士研究生.主要研究方向为issue标题自动生成.E-mail: yangjun1026@bistu.edu.cn" ]
  [ "刘诗凡男，1997年9月生，陕西汉中人.已于北京信息科技大学获得硕士学位，现为北京科技大学博士研究生.主要研究方向为代码注释生成和软件测试.E-mail: pawn2017@bistu.edu.cn" ]
  [ "陈翔男，1980年3月生，江苏南通人.现为南通大学副教授，硕士生导师.主要研究方向为软件缺陷预测、软件缺陷定位、回归测试和组合测试.E-mail: xchencs@ntu.edu.cn" ]
  [ "崔展齐男，1984年2月生，贵州金沙人.现为北京信息科技大学教授，博士生导师.主要研究方向为软件分析与软件测试技术.E-mail: czq@bistu.edu.cn" ]
- 基金信息：
  
  江苏省前沿引领技术基础研究专项(BK20202001);北京信息科技大学“勤信人才”培育计划项目(QXTCPB202406)
- DOI：10.12263/DZXB.20240434
  中图分类号： TP311.5
- 收稿：2024-05-10，
  
  修回：2025-02-21，
  
  纸质出版：2025-05-25
- 稿件说明：
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杨君, 刘诗凡, 陈翔, 等. GITG：面向Gitee平台的issue标题自动生成方法[J]. 电子学报, 2025, 53(05): 1559-1570.

YANG Jun, LIU Shi-fan, CHEN Xiang, et al. GITG: Automatic Issue Title Generation Method for Gitee Platform[J]. Acta Electronica Sinica, 2025, 53(05): 1559-1570.
杨君, 刘诗凡, 陈翔, 等. GITG：面向Gitee平台的issue标题自动生成方法[J]. 电子学报, 2025, 53(05): 1559-1570. DOI：10.12263/DZXB.20240434

YANG Jun, LIU Shi-fan, CHEN Xiang, et al. GITG: Automatic Issue Title Generation Method for Gitee Platform[J]. Acta Electronica Sinica, 2025, 53(05): 1559-1570. DOI：10.12263/DZXB.20240434

摘要

在开源软件和开源平台中，开发人员可以通过提交issue来记录所发现的软件错误或提出新功能需求.由于缺乏经验、专业水平有限等原因，用户可能无法对issue内容进行准确有效地总结，导致issue标题质量较低，进而降低issue的解决效率.此外，现有的issue标题自动生成方法主要面向GitHub等英文开源平台，当应用在Gitee等国产开源平台时表现不佳.同时，现有方法主要使用issue主体描述作为输入，忽略了issue中的代码片段等重要信息.为此，本文提出一种面向Gitee平台的issue标题自动生成方法GITG（Gitee Issue Title Generation），针对包含中文和英文文本的issue，使用构建的Gitee issue数据集对支持中文的预训练模型Chinese BART（Bidirectional and Auto-Regressive Transformers）进行微调，利用issue主体描述和代码片段的双模态信息来自动生成issue标题.为验证GITG的有效性，构建了包含18 242个Gitee issue样本的数据集.实验结果表明，GITG在ROUGE-1、ROUGE-2和ROUGE-L指标上相较于iTAPE和iTiger分别至少提升了13.09%、10.18%和12.84%，在BLEU和METEOR指标上同样取得了性能提升.人工评价结果表明，GITG生成标题的平均得分在整体分数、流畅性、信息性和简洁性4个评价指标上相较iTAPE和iTiger分别至少提升了26.7%、20.8%、24.2%和20.0%.

Abstract

In open-source software and platforms

developers can submit issues to report software bugs or suggest new feature requests. Due to the lack of experience and limited professional skills

users may struggle to summarize the content of issues accurately and effectively

resulting in low-quality issue titles

which in turn decreases the efficiency of addressing issues. Additionally

existing automatic issue title generation methods are primarily designed for English open-source platforms

such as GitHub

and the performance are degraded when applied to Chinese open-source platforms

like Gitee. Furthermore

existing methods mainly use the issue body description as inputs

ignoring the code snippets in the issue. In this paper

we propose a method called GITG (Gitee Issue Title Generation) specifically designed for Gitee

an open-source platform. GITG addresses the challenge of generating issue titles for both Chinese and English text by fine-tuning the Chinese BART (Bidirectional and Auto-Regressive Transformers) pre-trained model on a constructed Gitee issue dataset. It leverages the bi-modal information from the issue body description and code snippets to generate informative and accurate issue titles. A dataset consisting of 18 242 Gitee issue samples is constructed to validate the effectiveness of GITG. Experimental results demonstrate that GITG outperforms iTAPE and iTiger by at least 13.09%

10.18%

and 12.84% on the ROUGE-1

ROUGE-2

and ROUGE-L metrics

respectively. GITG also achieves improvements in BLEU and METEOR metrics. Human evaluation results also indicate that the average scores of the titles generated by GITG are improved by at least 26.7%

20.8%

24.2%

and 20.0% in overall score

fluency

informativeness

and conciseness

respectively

compared to iTAPE and iTiger.

关键词

Keywords

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