Application Test Automate Generation Using Natural Language Processing and Machine Learning

Data is received that encapsulate a test case document including a series of test instructions written in natural language for testing a software application. The software application includes a plurality of graphical user interface views (e.g., views in a web browser, etc.). Thereafter, the test case document is parsed using at least one natural language processing algorithm. This parsing includes tagging instructions in the test case document with one of a plurality of pre-defined sequence labels. Subsequently, a test automate is generated using at least one machine learning model trained using historical test case documents, corresponding automates, and their successful executions and based on the tagged instructions in the test case document. The generated test automate includes one or more test scripts which, when executed, perform a testing sequence of the software application according to the series of test instructions. Related apparatus, systems, techniques and articles are also described.