Transformer-based Bug/Feature Classification

被引：0
|
作者：
Ozturk, Ceyhun E. [1 ]
Yilmaz, Eyup Halit [2 ]
Koksal, Omer [2 ,3 ]
机构：
[1] Bilkent Univ, Elekt & Elekt Muhendisligi Bolumu, ASELSAN Arastirma Merkezi, Ankara, Turkiye
[2] ASELSAN Arastirma Merkezi, Ankara, Turkiye
[3] Univ Doha Sci & Technol, Data Sci & Artificial Intelligence Dept, Doha, Qatar
来源：
2023 31ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU | 2023年
关键词：
Software bug report classification; natural language processing; pre-trained language models; BERT;
D O I：
10.1109/SIU59756.2023.10223806
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Automatic classification of a software bug report as a 'bug' or 'feature' is essential to accelerate closed-source software development. In this work, we focus on automating the bug/feature classification task with artificial intelligence using a newly constructed dataset of Turkish software bug reports collected from a commercial project. We train and test support vector machine (SVM), k-nearest neighbors (KNN), convolutional neural network (CNN), transformer-based models, and similar artificial intelligence models on the collected reports. Results of the experiments show that transformer-based BERTurk is the best-performing model for the bug/feature classification task.
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页数：4
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