一句话：BEE 利用机器学习自动标注 bug 报告中的 OB、EB 与 S2R 并检测缺失信息。
用户编写的 bug 报告常缺少结构化信息，报告者与开发者难以判断系统行为、期望行为和复现步骤。BEE 自动分析 bug 报告并反馈其观测行为（OB）、期望行为（EB）与复现步骤（S2R）。它用机器学习判断 issue 是 bug、增强还是问题，为句子标注 OB/EB/S2R，并检测缺失要素。BEE 集成 GitHub 并提供公共 Web API。作者在超过 5 千份现有 bug 报告上评估模型，发现能正确检测 OB、EB、S2R 句子及缺失信息。BEE 已开源。
This paper introduces BEE, a tool that automatically analyzes user-written bug reports and provides feedback to reporters and developers about the system’s observed behavior (OB), expected behavior (EB), and the steps to reproduce the bug (S2R). BEE employs machine learning to (i) detect if an issue describes a bug, an enhancement, or a question; (ii) identify the structure of bug descriptions by automatically labeling the sentences that correspond to the OB, EB, or S2R; and (iii) detect when bug reports fail to provide these elements. BEE is integrated with GitHub and offers a public web API that researchers can use to investigate bug management tasks based on bug reports. We evaluated BEE’s underlying models on more than 5k existing bug reports and found they can correctly detect OB, EB, and S2R sentences as well as missing information in bug reports. BEE is an open-source project that can be found at https://git.io/JfFnN. A screencast showing the full capabilities of BEE can be found at https://youtu.be/8pC48f_hClw.
DOI 原文 ·
@inproceedings{paperbot5847,
title = {BEE: a tool for structuring and analyzing bug reports},
author = {Yang Song and Oscar Chaparro},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3417928}
}