Towards reproducible research: automatic classification of empirical requirements engineering papers
Towards reproducible research: automatic classification of empirical requirements engineering papers
复制标题
迈向可重复的研究：经验需求工程论文的自动分类
DOI：
10.1145/3190645.3190689
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Griffioen, Sarah
中科院分区：
文献类型：
--
作者：
Woodson, Clinton;Hayes, Jane Huffman;Griffioen, Sarah
研究必须是可复制的，以便对科学产生影响，并为我们领域的知识体系做出贡献。然而，研究表明，70%的学术实验室研究无法复制。在软件工程中，更具体地说，需求工程（RE），可重复的研究是罕见的，数据集并不总是可用的或方法没有完全描述。缺乏可重复的研究阻碍了进展，研究人员不得不从头开始复制实验。从RE开始的研究人员必须筛选会议论文，找到经验性的论文，然后必须查看经验性论文中的可用数据（如果有的话），以初步确定该论文是否可以复制。本文解决了这个问题的两个部分，确定RE文件和确定实证文件内的RE文件。最近的RE和经验会议论文被用来学习功能，并建立一个自动分类器，以识别RE和经验的论文。我们介绍了经验需求研究分类器（ERRC）方法，该方法使用自然语言处理和机器学习来对会议论文进行监督分类。我们将我们的方法与基于关键字的基线方法进行比较。为了评估我们的方法，我们研究了IEEE需求工程会议和IEEE软件测试与分析国际研讨会的论文集。我们发现，ERRC方法在所有情况下，但在少数情况下比基线方法更好。
Research must be reproducible in order to make an impact on science and to contribute to the body of knowledge in our field. Yet studies have shown that 70% of research from academic labs cannot be reproduced. In software engineering, and more specifically requirements engineering (RE), reproducible research is rare, with datasets not always available or methods not fully described. This lack of reproducible research hinders progress, with researchers having to replicate an experiment from scratch. A researcher starting out in RE has to sift through conference papers, finding ones that are empirical, then must look through the data available from the empirical paper (if any) to make a preliminary determination if the paper can be reproduced. This paper addresses two parts of that problem, identifying RE papers and identifying empirical papers within the RE papers. Recent RE and empirical conference papers were used to learn features and to build an automatic classifier to identify RE and empirical papers. We introduce the Empirical Requirements Research Classifier (ERRC) method, which uses natural language processing and machine learning to perform supervised classification of conference papers. We compare our method to a baseline keyword-based approach. To evaluate our approach, we examine sets of papers from the IEEE Requirements Engineering conference and the IEEE International Symposium on Software Testing and Analysis. We found that the ERRC method performed better than the baseline method in all but a few cases.
影响因子：
4.1
作者：
Dit, Bogdan;Moritz, Evan;Cleland-Huang, Jane
通讯作者：
Cleland-Huang, Jane
衡量需求质量以预测可测试性
DOI：
--
发表时间：
2015
期刊：
International Workshop on Artificial Intelligence for Requirements Engineering
影响因子：
--
作者：
J. Hayes;Wenbin Li;Tingting Yu;Xue Han;Mark Hays;Clinton Woodson
通讯作者：
Clinton Woodson
实证研究
DOI：
--
发表时间：
2020
期刊：
Death in Custody
影响因子：
--
作者：
E. Njoku
通讯作者：
E. Njoku