In this study, we apply co-word analysis - a text mining technique based on the co-occurrence of terms - to map the topology of software testing research topics, with the goal of providing current and prospective researchers with a map, and observations about the evolution, of the software testing field. Our analysis enables the mapping of software testing research into clusters of connected topics, from which emerge a total of 16 high-level research themes and a further 18 subthemes. This map also suggests topics that are growing in importance, including topics related to web and mobile applications and artificial intelligence. Exploration of author and country-based collaboration patterns offers similar insight into the implicit and explicit factors that influence collaboration and suggests emerging sources of collaboration for future work. We make our observations - and the underlying mapping of research topics and research collaborations - available so that researchers can gain a deeper understanding of the topology of the software testing field, inspiration regarding new areas and connections to explore, and collaborators who will broaden their perspectives.
翻译:在这项研究中,我们应用了共同语言分析 -- -- 一种基于术语共生的文字采矿技术 -- -- 来绘制软件测试研究专题的地形图,目的是向当前和未来的研究人员提供软件测试领域演变情况的地图和观测结果。我们的分析有助于将软件测试研究映射成相互关联的专题组群,从中共产生16个高级别研究主题和另外18个次主题。这个地图还提出了越来越重要的专题,包括与网络和移动应用以及人工智能有关的专题。作者和国家协作模式的探索,对影响合作的隐含和明确因素提供了类似的洞察力,并为未来工作提出了新的合作来源。我们提供了我们的意见 -- -- 以及研究专题和研究协作的基本绘图 -- -- 以便研究人员能够更深入地了解软件测试领域的地形,对新领域和新联系的启发,以及扩大他们视野的合作者。