Automatic Test Case Generation Mechanism with Natural Language based Korean Requirements

Abstract In software industry fields, software testers absolutely force on how to make test cases with Informal or formal requirements. In software validation perspective, to do user acceptance testing, we should manually have been created numerous test case to meet the desired test coverage based on the precise analysis of clear, unstructured requirements. Until now, defining and analysing the meaning of natural language-based requirements is challenging, and automation is also difficult. This is, rare research does generate test case with natural language requirements sentences especially in Korea. To solve this problem, we propose a mechanism for automatically generating test cases from natural language requirements. This approach involves (1) simplifying unstructured Korean requirements, (2) normalizing requirement sentences, (3) modelling the requirement simplification process (C3Tree modelling), (4) automatically generating cause-effect graph model, and then test cases via decision table model with the graph models through meta-model transformation methods, (5) semi-automatic generation of test scripts, (6) generating test result reports, and (7) finally automatic generation of requirement traceability matrices. Ultimately, simplifying unstructured Korean requirements sentences aids in understanding requirements among stakeholders and reduces test costs.