Mining Discriminative Issue Resolution Temporal Sequential Patterns in Open Source Software Repositories
Resolving issue reports is essential to the development of open-source software, which helps developers collaborate, communicate, and fix bugs, thereby ensuring the quality and functionality of the software. While previous studies focus on the static factors that impact the issue resolution time, few of them investigate the issue resolution patterns. In this paper, we propose an approach based on discriminative sequential pattern mining to discover the patterns in issue resolution processes with different efficiency. We devise an issue resolution process modeling method, which adds events’ time interval values to the activity sequences, thus providing a richer understanding of the issue resolution. And we find that there are several typical patterns in the issue resolution process with different lifetimes. To guide developers in prioritizing issues and allocating resources, we propose a prediction model and a recommendation approach based on the mined patterns. We extract features from patterns and construct random forest classifier models to predict the resolution speed of issues (fast, normal, or slow). The accuracy of our models reaches 70% or higher in most repositories and mixed repositories’ data, indicating a great performance. Finally, we recommend actions to users participating in ongoing issues at an early stage based on the mined patterns, thus improving the efficiency of the issue resolution process.
Sat 21 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 17:30 | Session6: AI for Software Engineering IIResearch Track at Cosmos 3A Chair(s): Xing Hu Zhejiang University | ||
16:00 15mTalk | Beyond Isolated Changes: A Context-aware and Dependency-enhanced Code Change Detection Method Research Track Binghe Wang Xi’an Jiaotong University, Wuxia Jin Xi'an Jiaotong University, Zijun Wang Northwest University, Mengjie Sun Xi’an Jiaotong University, Haijun Wang Xi'an Jiaotong University | ||
16:15 15mTalk | Orion: A Multi-Agent Framework for Optimizing RAG Systems through Specialized Agent Collaboration Research Track xianxing fang Xidian University, Liangru Xie Xidian University, Weibin Yang Xidian University, Tianyi Zhang Xidian University, Zhang Ruitao Xi’an Jiaotong-Liverpool University, Hao Wang Xidian University, Di Wu Norwegian University of Science and Technology, Yushan Pan Xi'an Jiaotong-Liverpool University File Attached | ||
16:30 15mTalk | GPT Store Mining and Analysis Research Track Dongxun Su Huazhong University of Science and Technology, Yanjie Zhao Huazhong University of Science and Technology, Xinyi Hou Huazhong University of Science and Technology, Shenao Wang Huazhong University of Science and Technology, Haoyu Wang Huazhong University of Science and Technology | ||
16:45 15mTalk | Mining Discriminative Issue Resolution Temporal Sequential Patterns in Open Source Software Repositories Research Track YaxinWang Nanjing University, Liang Wang Nanjing University, Hao Hu Nanjing University, Xianping Tao Nanjing University | ||
17:00 15mTalk | Generating SysML Behavior Models via Large Language Models: an Empirical Study Research Track Yuan Wang School of Software, Beihang University, Ning Ge School of Software, Beihang University, Jiangxi Liu Beihang University, Zhilong Cao Beihang University, Zheping Chen Beihang University, Chunming Hu Beihang University | ||
17:15 15mTalk | FIRE: Smart Contract Bytecode Function Identification via Graph-Refined Hybrid Feature Encoding Research Track |
Cosmos 3A is the first room in the Cosmos 3 wing.
When facing the main Cosmos Hall, access to the Cosmos 3 wing is on the left, close to the stairs. The area is accessed through a large door with the number “3”, which will stay open during the event.