Journal of Computer Science Original Research Paper Measuring Test Data Uniformity in Acceptance Tests for the FitNesse and Gherkin Notations Douglas Hiura Longo, Patrícia Vilain and Lucas Pereira da Silva Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil Article history Abstract: This paper presents two metrics designed to measure the data Received: 23-11-2020 uniformity of acceptance tests in FitNesse and Gherkin notations. The Revised: 24-02-2021 objective is to measure the data uniformity of acceptance tests in order Accepted: 25-02-2021 to identify projects with lots of random and meaningless data. Random data in acceptance tests hinder communication between stakeholders Corresponding Author: Douglas Hiura Longo and increase the volume of glue code. The main contribution of this Department of Informatics and paper is the implementation of the proposed metrics. This paper also Statistics, Federal University of evaluates the uniformity of test data from several FitNesse and Gherkin Santa Catarina, Florianópolis, projects found on GitHub, as a means to verify if the metrics are Brazil applicable. First, the metrics were applied to 18 FitNesse project Email:
[email protected] repositories and 18 Gherkin project repositories. The measurements taken from these repositories were used to present cases of irregular and uniform test data. Then, we have compared the notations from FitNesse and Gherkin in terms of projects and features. In terms of projects, no significant difference was observed, that is, FitNesse projects have a level of uniformity similar to Gherkin projects. However, in terms of features and test documents, there was a significant difference.