错误报告优先级分类模型。复制研究

IF 2 2区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Automated Software Engineering Pub Date : 2024-04-10 DOI:10.1007/s10515-024-00432-1
Andreea Galbin-Nasui, Andreea Vescan
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引用次数: 0

摘要

错误跟踪系统每天都会收到大量的错误。保持软件的完整性和生产高质量软件的过程充满挑战。错误分类过程通常是一项人工任务,可能会导致人为错误并耗费大量时间。本研究有两个目的:首先,对错误报告优先级分类方法进行文献综述;其次,使用各种分类器复制现有方法,以提取关于优先级分类方法的新见解。我们采用了系统文献综述的方法来确定与错误报告优先级分类问题相关的最相关的现有方法。此外,我们还对三种分类器进行了复制研究:Naive Bayes (NB)、支持向量机 (SVM) 和卷积神经网络 (CNN)。我们进行了两组实验:第一组是我们自己基于 NB 和 CNN 的 NLTK 实现,第二组是基于 Weka 实现的 NB、SVM 和 CNN。使用的数据集包括几个 Eclipse 项目和一个与数据库系统相关的项目。对于 CNN 分类器来说,错误优先级 P3 得到的结果更好,总体而言,三种分类器之间的质量关系与最初的研究结果相同。复制研究证实了原始研究的结果,强调了进一步研究用作训练的项目和用作测试的项目的特征之间的关系的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Bug reports priority classification models. Replication study

Bug tracking systems receive a large number of bugs on a daily basis. The process of maintaining the integrity of the software and producing high-quality software is challenging. The bug-sorting process is usually a manual task that can lead to human errors and be time-consuming. The purpose of this research is twofold: first, to conduct a literature review on the bug report priority classification approaches, and second, to replicate existing approaches with various classifiers to extract new insights about the priority classification approaches. We used a Systematic Literature Review methodology to identify the most relevant existing approaches related to the bug report priority classification problem. Furthermore, we conducted a replication study on three classifiers: Naive Bayes (NB), Support Vector Machines (SVM), and Convolutional Neural Network (CNN). Two sets of experiments are performed: first, our own NLTK implementation based on NB and CNN, and second, based on Weka implementation for NB, SVM, and CNN. The dataset used consists of several Eclipse projects and one project related to database systems. The obtained results are better for the bug priority P3 for the CNN classifier, and overall the quality relation between the three classifiers is preserved as in the original studies. The replication study confirmed the findings of the original studies, emphasizing the need to further investigate the relationship between the characteristics of the projects used as training and those used as testing.

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来源期刊
Automated Software Engineering
Automated Software Engineering 工程技术-计算机:软件工程
CiteScore
4.80
自引率
11.80%
发文量
51
审稿时长
>12 weeks
期刊介绍: This journal details research, tutorial papers, survey and accounts of significant industrial experience in the foundations, techniques, tools and applications of automated software engineering technology. This includes the study of techniques for constructing, understanding, adapting, and modeling software artifacts and processes. Coverage in Automated Software Engineering examines both automatic systems and collaborative systems as well as computational models of human software engineering activities. In addition, it presents knowledge representations and artificial intelligence techniques applicable to automated software engineering, and formal techniques that support or provide theoretical foundations. The journal also includes reviews of books, software, conferences and workshops.
期刊最新文献
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