A component recommendation model for issues in software projects

Kangwanwisit, Pacawat; Choetkiertikul, Morakot; Ragkhitwetsagul, Chaiyong; Kula, Raula Gaikovina; Maipradit, Rungroj; Hata, Hideaki; Matsumoto, Kenichi

doi:10.1109/jcsse54890.2022.9836311

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…The baseline models [7], [20], [35], [38] consist of a word-based method with its own vocabulary, which might not consist of suitable information for predicting components in new issues. Thus, the proposed fine-tuned pretrained language models predict components using the sequence information from the sentence, which can respond to the new issues correctly.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Component Prediction for Issue Reports Using Fine-Tuned Pretrained Language Models

Wang

Lee

2022

IEEE Access

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Various issues or bugs are reported during the software development. It takes considerable effort, time, and cost for the software developers to triage these issues manually. Many previous studies have proposed various method to automate the triage process by predicting component using word-based language models. However, these methods still suffer from unsatisfactory performance due to their structural limitations and ignorance of the word context. In this paper, we propose a novel technique based on pretrained language models and it aims to predict a component of an issue report. Our approach fine-tunes the pretrained language models to conduct multilabel classifications. The proposed approach outperforms the previous state-of-the-art method by more than 30% with respect to the recall at k on all the datasets considered in our experiment. This improvement suggests that fine-tuned pretrained language models can help us to predict issue components effectively.

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Section: Resultsmentioning

confidence: 99%

“…Kangwanwisit et al [20] conducted various word-based experiments to find proper feature extraction and classification techniques for the component prediction. For the feature extraction task, various techniques such as bag of word (BoW) [42], N-gram [5] IDF, and TF-IDF were considered.…”

Section: Related Workmentioning

confidence: 99%

Automatic Component Prediction for Issue Reports Using Fine-Tuned Pretrained Language Models

Wang

Lee

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

A component recommendation model for issues in software projects

Cited by 1 publication

References 16 publications

Automatic Component Prediction for Issue Reports Using Fine-Tuned Pretrained Language Models

Automatic Component Prediction for Issue Reports Using Fine-Tuned Pretrained Language Models

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