Automatically Identifying Bug Entities and Relations for Bug Analysis
Dingshan Chen, Bin Li, Cheng Zhou, Xuanrui Zhu · 2019
During the bug fixing process, developers usually analyze the historical relevant bug reports in bug repository to support various bug analysis activities, i.e., bug understanding, bug localization, bug fixing, etc. There are rich semantics and relations in the bug reports, which are useful for bug analysis. Entity recognition and relation extraction are useful to represent semantics and relations in the text. However, the text in the bug reports are often in a free-style form, and include lots of noisy information. Therefore, how to extract and express rich semantics and relations in the bug reports is difficult, but important for bug analysis. To address this challenge, we propose an approach, which incorporates the neural networks RNN with dependency parser to automatically extract bug entities and their relations from bug reports. A preliminary empirical evaluation demonstrates that our approach is effective to extract bug entities and their relations from bug reports in the bug repository.