FAST$^2$: Better Automated Support for Finding Relevant SE Research Papers
FAST$^2$: Better Automated Support for Finding Relevant SE Research Papers
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FAST$^2$：更好地自动支持查找相关 SE 研究论文
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Menzies
中科院分区：
文献类型：
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作者：
Zhe Yu;T. Menzies
文献综述是必不可少的任何研究人员试图跟上新兴的软件工程文献。FAST$^2$是一种新颖的工具，通过帮助研究人员找到下一篇有希望阅读的论文（在一组未读论文中）来减少进行文献综述所需的工作量。本文描述了FAST$^2$，并在Wahono（2015），Hall（2012），Radjenovi 'c（2013）和Kitchenham（2017）进行的四个大型软件工程文献综述中进行了测试。我们发现，FAST $^2 $是一个更快，更强大的工具，以帮助研究人员找到相关的SE论文，可以弥补人类在审查过程中所犯的错误。FAST$^2$的有效性可以归因于三个关键创新：（1）一种新的应用外部领域知识的方式（简单的两三个关键词搜索）来指导论文的初步选择-这有助于更快地找到相关的研究论文，减少差异;（2）估计尚未找到的剩余相关论文的数量-在实际环境中，可用于决定是否需要终止审查过程;（3）一种新的自校正分类算法-在研究人员错误分类论文的情况下，自动校正自己。
Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. FAST$^2$ is a novel tool for reducing the effort required for conducting literature reviews by assisting the researchers to find the next promising paper to read (among a set of unread papers). This paper describes FAST$^2$ and tests it on four large software engineering literature reviews conducted by Wahono (2015), Hall (2012), Radjenovi'c (2013) and Kitchenham (2017). We find that FAST$^2$ is a faster and robust tool to assist researcher finding relevant SE papers which can compensate for the errors made by humans during the review process. The effectiveness of FAST$^2$ can be attributed to three key innovations: (1) a novel way of applying external domain knowledge (a simple two or three keyword search) to guide the initial selection of papers---which helps to find relevant research papers faster with less variances; (2) an estimator of the number of remaining relevant papers yet to be found---which in practical settings can be used to decide if the reviewing process needs to be terminated; (3) a novel self-correcting classification algorithm---automatically corrects itself, in cases where the researcher wrongly classifies a paper.