BugHunter: Temporal Bug Prediction Dataset with Code Metrics and Bug Lifecycle Data

Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc., therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning.

We present the BugHunter Dataset: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74.

受代码频繁变更、交付时限紧张等因素影响，软件开发过程中代码缺陷（bug）难以完全避免，因此配备用于检测这类错误的工具至关重要。一种缺陷识别方案为：分析过往存在缺陷的源代码元素的特征，并基于此类特征对当前代码的缺陷进行预测，例如可借助机器学习模型完成此类任务。为支撑模型构建工作，研究人员会将源代码元素及其特征收录至所谓的缺陷数据集（bug dataset）中，这类数据集将作为模型训练的输入数据。
本次研究推出BugHunter数据集（BugHunter Dataset）：这是一种全新的自动构建且可免费获取的缺陷数据集，涵盖源代码元素（文件、类、方法）以及多维度代码度量指标与缺陷相关信息。当前已有的其他缺陷数据集均遵循传统范式：仅在代码的一个或多个预选定发布版本中，收集所有源代码元素（无论是否存在缺陷）的特征。而本研究的方法则不受发布版本限制，从可识别的缺陷存在的最窄时间窗口内，捕获同一源代码元素的缺陷状态与修复后状态。为验证该新型数据集的实用价值，我们构建并评估了缺陷预测模型，最终取得了F度量（F-measure）超过0.74的实验结果。

搜集汇总

背景与挑战

背景概述

BugHunter是一个用于时间错误预测的数据集，包含代码元素（文件、类、方法）的代码度量和错误生命周期数据，能捕获错误出现时的具体状态。该数据集通过自动构建，支持机器学习模型训练，并在错误预测中实现了F-measure超过0.74的性能表现。