Test case generation and history data analysis during optimization in regression testing: An NLP study

Abstract

The generation of test cases to verify and validate the actions of software or an application, as per the customers’ requirements, is an indispensable activity in software industries. A tester could construct test cases to suffice various objectives, which could be random or task-oriented at times. Most of the time, test cases are generated based on clients’ specifications or requirements. These requirements are structured in natural language, and manual derivation of test cases from such client-stated requirements could be a cumbersome and time-absorbing activity for testers. Until recently, many practitioners have proposed a natural language processing (NLP)-oriented solution to automate or semi-automate the manual process of generating test cases from requirements; nevertheless, such studies imposed a restriction on how the clients should document or represent their requirements. This study, on the contrary, suggested an NLP solution that considers free-format user requirements and applies text pre-processing, a combination of dependency parser and RAKE process, along with a statistical similarity measure and template-based natural language generation (NLG) to translate them into detailed test cases. Apart from test case generation, with the aid of NLP tactics, this study has also proposed a solution for encoding the historical data of test cases into numerical values. Such numerical scores serve as valuable data and create the proper insight for testers during test case optimization.