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 (LLM) present new opportunities for enhancing prediction accuracy. This study systematically evaluates 18 LLM variants (including BERT, 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 percent though it may introduce bias; (2) LoRA fine-tuned Llama 3.1 8B Instruct further improve performance, reaching 79 percent accuracy and significantly enhancing recall for approved reports (76.1 percent vs. LSTM-GLOVE's 64.1 percent), outperforming traditional methods by 5 percent under strict chronological evaluation and effectively addressing class imbalance issues. These findings establish LLM 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.