Replication Package for: Enhancing Task Prioritization in Software Development Issues Tracking System

This Zenodo record provides the code, scripts, notebooks, and links to datasets and models used to support the findings for Research Questions 1 through 5 (RQ1-RQ5) in the paper "Enhancing Task Prioritization in Software Development Issues Tracking system." Modern software development faces a critical bottleneck in manually prioritizing issues. This paper investigates automated issue priority classification using Transformer models. We evaluate models like BERT, DeBERTa, and a specialized ModernBERT, comparing them against general Large Language Models (LLMs) such as Qwen2.5-3B and Llama-3.2-3B, using curated datasets from Jira and GitHub. This package contains the necessary components to replicate the in-distribution classification (RQ1), out-of-distribution generalization (RQ2), fine-tuning impact assessments (RQ3), detailed performance analysis of ModernBERT across priority levels (RQ4), and the comparative performance of LLMs against ModernBERT (RQ5). The main paper demonstrates that Transformer models, particularly ModernBERT, achieve high classification performance (e.g., accuracy > 81%, AUC > 0.90, MCC > 0.62), significantly outperforming the evaluated general LLMs for this task.