Enhancing Task Prioritization in Software Development Issues Tracking system

Enhancing Task Prioritization in Software Development Issues Tracking system | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Journal of Software: Evolution and Process This is a preprint and has not been peer reviewed. Data may be preliminary. 28 May 2025 V1 Latest version Share on Enhancing Task Prioritization in Software Development Issues Tracking system Authors : Karthik Shivashankar 0009-0001-8508-2978 [email protected] , Kristian Marison Haugerud , and Antonio Martini Authors Info & Affiliations https://doi.org/10.22541/au.174844655.55019809/v1 Published Journal of Software: Evolution and Process Version of record Peer review timeline 289 views 205 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Modern software development faces a critical bottleneck in manually prioritizing the overwhelming volume of issues generated in platforms like Jira and GitHub. This labor-intensive process leads to delays, increased costs, inconsistent handling, and developer burnout, worsened by the common lack of standardized priority labels. This paper investigates the potential of automated issue priority classification using state-of-the-art Transformer models to alleviate this burden. We evaluate the performance of models like BERT, DeBERTa, and ModernBERT, comparing them against general Large Language Models (LLMs) such as Qwen2.5-3B and Llama-3.2-3B, using curated datasets derived from public Jira and GitHub repositories. Our research addresses the effectiveness of these models for in-distribution classification, their generalization capabilities on out-of-distribution projects, the impact of fine-tuning, and performs a detailed performance comparison across different priority levels and model types. Results demonstrate that Transformer models, particularly ModernBERT, achieve high classification performance (e.g., accuracy > 81%), significantly outperforming the evaluated general LLMs (accuracy 75%) for this specific task. We find that binary classification is more effective than multilabel approaches, models generalize well to unseen projects, and performance is further enhanced by fine-tuning. Key contributions include the provision of cleaned, labeled datasets and a comprehensive evaluation confirming the viability and benefits of using specialized Transformer models for automated issue priority suggestion, offering a path to improved efficiency and resource allocation in software development workflows. Supplementary Material File (priority_journal_updated.pdf) Download 697.40 KB Information & Authors Information Version history V1 Version 1 28 May 2025 Peer review timeline Published Journal of Software: Evolution and Process Version of Record 30 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Journal of Software: Evolution and Process Keywords nlp prioritization software engineering source tracker issues Authors Affiliations Karthik Shivashankar 0009-0001-8508-2978 [email protected] Universitetet i Oslo Institut for Informatikk View all articles by this author Kristian Marison Haugerud Universitetet i Oslo Institut for Informatikk View all articles by this author Antonio Martini Universitetet i Oslo Institut for Informatikk View all articles by this author Metrics & Citations Metrics Article Usage 289 views 205 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Karthik Shivashankar, Kristian Marison Haugerud, Antonio Martini. Enhancing Task Prioritization in Software Development Issues Tracking system. Authorea . 28 May 2025. DOI: https://doi.org/10.22541/au.174844655.55019809/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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