Duplicate Bug Report Detection by Using Sentence Embedding and Faiss

Abstract

Duplicate issue reports in the issue management system can cause unnecessary work for users and developers and disrupt the progress of their work. Therefore, researchers have developed duplicate report detection techniques. However, most of those techniques require training pairs of duplicate reports as well as those of non-duplicate bug reports. To speed up the processing of such machine learning-oriented methods, we propose implementing duplicate report detection using the recent technologies Sentence Bert and Faiss. By using the Faiss library, we can quickly detect duplicate issue reports. We also evaluate our proposed approaches with two different experiments and discuss our future work. © 2023 Copyright for this paper by its authors.