AttentiveBugLocator: A Bug Localization Model using Attention-based SemanticFeatures and Information Retrieval
Aminu A. Ahmad,
Lasheng Yu,
Mohamed Kholief
et al.
Abstract:In recent years, deep learning-based algorithms such as CNN, LSTM, and autoencoders have been proposed to rank suspicious buggy files. Meanwhile,representational learning has served to be the best approach to extract rich semantic features of bug reports and source code to reduce their lexical mismatch. In this paper, we propose AttentiveBugLocator, a Siamese-based representational learning modelfor improved bug localization performance. AttentiveBugLocator employs BERT and code2vec embedding models to produce… Show more
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