Generating Abstract Test Cases from User Requirements using MDSE and NLP

Model-driven software engineering (MDSE) has emerged as a popular and commonly used method for designing software systems in which models are the primary development artifact over the last decade. MDSE has resulted in the trend toward further automating the software process. However, the generation of test cases from user requirements still lags in reaching the required level of automation. Given that most user requirements are written in natural language, the recent advances in natural language processing (NLP) provide an opportunity to further automate the test generation process.In this paper, we exploit the advances in MDSE and NLP to generate abstract test cases from user requirements written in structured natural language and the respective data model. We accomplish this by creating meta-models for user requirements and abstract test cases and defining the appropriate transformation rules. To support this transformation, helper methods are defined to extract the relevant information from user requirements related to testing. To show the feasibility of the approach, we developed a prototype and conducted a case study with use cases and test cases from a Payroll Management System.