Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Bibliographic record
Abstract
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 unfinetuned 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 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".