On the Large-scale Graph Data Processing for User Interface Testing in Big Data Science Projects


Uygun Y., Oğuz R., Ölmezoğulları E., AKTAŞ M. S.

6th International Workshop to Improve Big Data Science Project Team Processes, 11 December 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/bigdata50022.2020.9378153
  • Keywords: Big Data Science Projects, Large-scale Data Processing, Dynamic URLs, NLP, Clustering, HDFS, Map Reduce, Word2vec, Software Testing

Abstract

In functional User Interface testing, test scenarios are written with respect to the requirements that are specified by test analysts. Usually, a test analyst focuses on base URLs and HTML components while collecting requirements of User Interface test scenarios. A base URL is essentially a unit segment of large scale graph data. It has mostly dynamic shape and is used to navigate pages amongst application's pages. We argue that even though dynamic URLs have additional important information about the content of the page, they are not being utilized in generating User Interface test scenarios. In this study, we address this lack of capability and focus on the development of a methodology that can support the usage of large-scale dynamic URL datasets in UI test script generation. Our proposed methodology is designed as an add-on tool that can be used on the top of the existing UI test automation tools to improve testing quality. We introduce a higher quality testing methodology to make the results more accurate, and we discuss the proposed methodology and give an overview of the implementation details followed by the evaluation results. We perform various performance evaluations to investigate how well the proposed algorithms scale under increasing data sizes. The results are promising and show the usability of the proposed methodology.