Context

To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area.

Objective

Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape.

Method

To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers.

Results

This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details).

Conclusion

This paper would benefit both practitioners and researchers by serving as an “index” to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners.

中文翻译：

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NLP辅助软件测试：文献的系统映射

语境

为了减少从自然语言需求中提取测试用例的人工工作，文献中提出了许多基于自然语言处理（NLP）的方法。考虑到该领域中的大量方法，并且由于许多从业者都渴望使用此类技术，因此综合并提供该领域的最新技术概述非常重要。

目的

我们的目标是总结NLP辅助软件测试中的最新技术，这可以使从业人员受益于潜在地利用那些基于NLP的技术。此外，这可以使研究人员对研究前景有一个总体了解。

方法

为了满足上述需求，我们以系统的文献作图（分类）形式进行了调查。在汇总了最初的95篇论文之后，我们进行了系统的投票，最终的论文集中包括67篇技术论文。

结果

这篇综述文章概述了论文中介绍的贡献类型，用于辅助软件测试的NLP方法类型，所需输入要求的类型以及对该领域中工具支持的概述。我们检测到的一些关键结果是：（1）本文提供的38种工具中只有4种（11％）可供下载；（2）较大比例的论文（67篇中有30篇）提供了对NLP方面的浅浅了解（几乎没有细节）。

结论

本文将作为该领域知识体系的“索引”，对从业人员和研究人员均有利。结果可以帮助从业人员利用现有的基于NLP的技术；这反过来降低了测试用例的设计成本，并减少了用于测试活动的人力资源。与我们的一些行业合作者共享此评论后，初步见识表明，此评论确实对从业者有用且有益。