Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers

Skryseth, Daniel; Shivashankar, Karthik; Pilán, Ildikó; Martini, Antonio

doi:10.1109/techdebt59074.2023.00017

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2024

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Cited by 3 publications

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Towards Enhancing Task Prioritization in Software Development Through Transformer-Based Issues Classification

Haugerud,

Shivashankar,

Martini

2024

Lecture Notes in Computer Science

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Towards Enhancing Task Prioritization in Software Development Through Transformer-Based Issues Classification

Haugerud,

Shivashankar,

Martini

2024

Lecture Notes in Computer Science

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From Reinvention to Reuse: An Empirical Example Study on Technical Debt Dataset

Rantala,

Mäntylä,

Sridharan

2024

Lecture Notes in Computer Science

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On the Identification of Self-Admitted Technical Debt with Large Language Models

Lambert,

Ishitani,

Xavier

2024

Anais Do XXXVIII Simpósio Brasileiro De Engenharia De Software (SBES 2024)

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Self-Admitted Technical Debt (SATD) refers to a common practice in software engineering involving developers explicitly documenting and acknowledging technical debt within their projects. Identifying SATD in various contexts is a key activity for effective technical debt management and resolution. While previous research has focused on natural language processing techniques and specialized models for SATD identification, this study explores the potential of Large Language Models (LLMs) for this task. We compare the performance of three LLMs - Claude 3 Haiku, GPT 3.5 turbo, and Gemini 1.0 pro - against the generalization of the state-of-the-art model designed for SATD identification. Additionally, we investigate the impact of prompt engineering on the performance of LLMs in this context. Our findings reveal that LLMs achieve competitive results compared to the state-of-the-art model. However, when considering the Matthews Correlation Coefficient (MCC), we observe that the LLM performance is less balanced, tending to score lower than the state-of-the-art model across all four confusion matrix categories. Nevertheless, with a well-designed prompt, we conclude that the models’ bias can be improved, resulting in a higher MCC score.

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Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers

Cited by 3 publications

References 20 publications

Towards Enhancing Task Prioritization in Software Development Through Transformer-Based Issues Classification

Towards Enhancing Task Prioritization in Software Development Through Transformer-Based Issues Classification

From Reinvention to Reuse: An Empirical Example Study on Technical Debt Dataset

On the Identification of Self-Admitted Technical Debt with Large Language Models

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