Home // ICSEA 2023, The Eighteenth International Conference on Software Engineering Advances // View article
Authors:
Qianru Meng
Xiao Zhang
Guus Ramackers
Visser Joost
Keywords: Duplicate Bug Detection; Deep Learning; Natural Language Processing; Transformer; Running Time; Accuracy
Abstract:
In the realm of Duplicate Bug Report Detection (DBRD), conventional methods primarily focus on statically analyzing bug databases, often disregarding the running time of the model. In this context, complex models, despite their high accuracy potential, can be time-consuming, while more efficient models may compromise on accuracy. To address this issue, we propose a transformer-based system designed to strike a balance between time efficiency and accuracy performance. The existing methods primarily address it as either a retrieval or classification task. However, our hybrid approach leverages the strengths of both models. By utilizing the retrieval model, we can perform initial sorting to reduce the candidate set, while the classification model allows for more precise and accurate classification. In our assessment of commonly used models for retrieval and classification tasks, sentence BERT and RoBERTa outperform other baseline models in retrieval and classification, respectively. To provide a comprehensive evaluation of performance and efficiency, we conduct rigorous experimentation on five public datasets. The results reveal that our system maintains accuracy comparable to a classification model, significantly outperforming it in time efficiency and only slightly behind a retrieval model in time, thereby achieving an effective trade-off between accuracy and efficiency.
Pages: 75 to 84
Copyright: Copyright (c) IARIA, 2023
Publication date: November 13, 2023
Published in: conference
ISSN: 2308-4235
ISBN: 978-1-68558-098-8
Location: Valencia, Spain
Dates: from November 13, 2023 to November 17, 2023