Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Enhancement reports (ERs) serve as a critical communication channel between users and developers, capturing valuable suggestions for software improvement. However, manually processing these reports is resource-intensive, leading to delays and potential loss of valuable insights. To address this challenge, enhancement report approval prediction (ERAP) has emerged as a research focus, leveraging machine learning techniques to automate decision-making. While traditional approaches have employed feature-based classifiers and deep learning models, recent advancements in large language models (LLMs) present new opportunities for enhancing prediction accuracy.
This study systematically evaluates 18 LLM variants (including RoBERTa, DeBERTa-v3, ELECTRA, and XLNet for encoder models; GPT-3.5-turbo, GPT-4o-mini, Llama 3.1 8B, Llama 3.1 8B Instruct and DeepSeek-V3 for decoder models) against traditional methods (CNN/LSTM-BERT/GloVe). Our experiments reveal two key insights: (1) Incorporating creator profiles increases unfine-tuned decoder-only models’ overall accuracy by 10.8% though it may introduce bias; (2) LoRA fine-tuned Llama 3.1 8B Instruct further improve performance, reaching 79% accuracy and significantly enhancing recall for approved reports (76.1% vs. LSTM-GLOVE’s 64.1%), outperforming traditional methods by 5% under strict chronological evaluation and effectively addressing class imbalance issues. These findings establish LLMs as a superior solution for ERAP, demonstrating their potential to streamline software maintenance workflows and improve decision-making in real-world development environments. We also investigated and summarized the ER cases where the large models underperformed, providing valuable directions for future research.
Sat 21 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
11:00 - 13:00 | Session7: AI for Software Engineering IIIResearch Track at Cosmos 3C Chair(s): Lina Gong Nanjing University of Aeronautics and Astronautic | ||
11:00 15mTalk | Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation Research Track Hongyu Kuang Nanjing University, Ning Zhang Nanjing University, Hui Gao Nanjing University, Xin Zhou Nanjing University, Wesley Assunção North Carolina State University, Xiaoxing Ma Nanjing University, Dong Shao Nanjing University, Guoping Rong Nanjing University, He Zhang Nanjing University | ||
11:15 15mTalk | DesDD: A Design-Enabled Framework with Dual-Layer Debugging for LLM-based Iterative API Orchestrating Research Track Zhuo Cheng Jiangxi normal University, Zhou Zou Jiangxi Normal University, Qing Huang School of Computer Information Engineering, Jiangxi Normal University, Zhenchang Xing CSIRO's Data61, Wei Zhang Jiangxi Meteorological Disaster Emergency Early Warning Center, Jiangxi Meteorological Bureau, Shaochen Wang Jiangxi Normal Univesity, Xueting Yi Jiangxi Meteorological Disaster Emergency Early Warning Center, Jiangxi Meteorological Bureau, Huan Jin School of Information Engineering, Jiangxi University of Technology, Zhiping Liu College of Information Engineering, Gandong University, Zhaojin Lu Jiangxi Tellhow Animation College, Tellhow Group Co.,LTD | ||
11:30 15mTalk | AUCAD: Automated Construction of Alignment Dataset from Log-Related Issues for Enhancing LLM-based Log Generation Research Track Hao Zhang Nanjing University, Dongjun Yu Nanjing University, Lei Zhang Nanjing University, Guoping Rong Nanjing University, YongdaYu Nanjing University, Haifeng Shen Southern Cross University, He Zhang Nanjing University, Dong Shao Nanjing University, Hongyu Kuang Nanjing University | ||
11:45 15mTalk | Enhancement Report Approval Prediction: A Comparative Study of Large Language Models Research Track | ||
12:00 15mTalk | MetaCoder: Generating Code from Multiple Perspectives Research Track chen xin , Zhijie Jiang National University of Defense Technology, Yong Guo National University of Defense Technology, Zhouyang Jia National University of Defense Technology, Si Zheng National University of Defense Technology, Yuanliang Zhang National University of Defense Technology, Shanshan Li National University of Defense Technology | ||
12:15 15mTalk | API-Repo: API-centric Repository-level Code Completion Research Track Zhihao Li State Key Laboratory for Novel Software and Technology, Nanjing University, Chuanyi Li Nanjing University, Changan Niu Software Institute, Nanjing University, Ying Yan State Key Laboratory for Novel Software and Technology, Nanjing University, Jidong Ge Nanjing University, Bin Luo Nanjing University | ||
12:30 15mTalk | AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length Research Track Junhang Cheng Beihang University, Fang Liu Beihang University, Chengru Wu Beihang University, Li Zhang Beihang University Pre-print Media Attached File Attached | ||
12:45 15mTalk | Lightweight Probabilistic Coverage Metrics for Efficient Testing of Deep Neural Networks Research Track Yining Yin Nanjing University, Yang Feng Nanjing University, Shihao Weng Nanjing University, Xinyu Gao , Jia Liu Nanjing University, Zhihong Zhao Nanjing University |
Cosmos 3C is the third 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.