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Published October 21, 2022 | Version 1.000
Dataset Open

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


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Background: Technical Debt (TD) needs to be controlled and tracked during software development. Current support, such as static analysis tools and even ML-based automatic tagging, is still ineffective, especially for context-dependent TD.

Aim: We study the usage of a large TD dataset in combination with cutting-edge Natural Language Processing (NLP) approaches to classify TD automatically in issue trackers, allowing the identification and tracking of informal TD conversations.

Method: We mine and analyse more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD-dataset). We then use our dataset to train state-of-the-art Transformer ML models, before performing a quantitative case study on three projects and evaluating the performance metrics during inference. Additionally, we study the adaptation of our model to classify context-dependent TD in an unseen project, by retraining the model including different percentages of the TD issues in the target project.

Results: (i) We provide GTD- dataset, the most comprehensive datasets of TD issues to date, including issues from 6,401 unique public repositories with various contexts;

(ii) By training state-of-the-art Transformers using the GTD-dataset, we achieve performance metrics that outperform previous approaches;

(iii) We show that our model can provide a relatively reliable tool to classify automatically TD in issue trackers, especially when adapted to unseen projects where the training includes a small portion of TD issues in the new project.

Conclusion: Our results indicate that we have taken significant steps towards closing the gap to practically and semi-automatically track TD issues in issue trackers.


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