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Identifying Technical Debt and Its Types Across Diverse Software Projects Issues by Shivashankar, Karthik; Orucevic, Mili; Kruke, Maren Maritsdatter; Martini, Antonio is a scholarly article available to read on EtoBox.

What is Identifying Technical Debt and Its Types Across Diverse Software Projects Issues about?

Technical Debt (TD) identification in software projects issues is crucial for maintaining code quality, reducing long-term maintenance costs, and improving overall project health. This study advances TD classification using transformer-based models, addressing the critical need for accurate and efficient TD identification in large-scale software development. Our methodology employs multiple binary classifiers for TD and its type, combined through ensemble learning, to enhance accuracy and robustness in detecting various forms of TD. We train and evaluate these models on a comprehensive dataset from GitHub Archive Issues (2015-2024), supplemented with industrial data validation. We demonstrate that in-project fine-tuned transformer models significantly outperform task-specific fine-tuned models in TD classification, highlighting the importance of project-specific context in accurate TD identification. Our research also reveals the superiority of specialized binary classifiers over multi-class models for TD and its type identification, enabling more targeted debt resolution strategies. A comparative analysis shows that the smaller DistilRoBERTa model is more effective than larger lan

Author

Shivashankar, Karthik; Orucevic, Mili; Kruke, Maren Maritsdatter; Martini, Antonio

Published

2024

Language

EN