GENERATION OF TEST MODELS FROM BEHAVIOR DRIVEN DEVELOPMENT SCENARIOS BASED ON BEHAVIOR DRIVEN DEVELOPMENT STEP DEFINITIONS AND SIMILARITY ANALYSIS USING NEURO LINGUISTIC PROGRAMMING AND MACHINE LEARNING MECHANISMS

摘要

Generation of test models from Behavior Driven Development scenarios based on Behavior Driven Development step definitions and similarity analysis using Neuro Linguistic Programming and machine learning mechanisms The present invention pertains to a method for automated verification of a software program in a Behavior-Driven Development (BDD) environment and a data processing system configured to execute such a method. Individual test steps of BDD test scenarios are first matched and then assigned to existing test step definitions from a BDD framework. If a one-to- one matching is not possible, natural language processing (NLP) is used to decide if an assignment is possible with a certain matching probability. The assigned test step definitions are used to generate graphical test models for the test scenarios, e.g. UML diagrams. Finally, executable test scripts are generated to test the software program. The present invention relates particularly to Behavior-Driven Development (BDD) and combines traditional BDD advantages with Model Based Testing (MBT) for improved convenience and automatization in case of complex software packages. The automated step matching allows an efficient mapping of BDD step phrases to a test automation framework and supports the structured development of the necessary framework code. The graphical test models add an additional abstraction layer and provide the opportunity to check BDD scenarios for consistency and completeness.