Automated Test Input Generation via Model Inference Based on User Story and Acceptance Criteria for Mobile Application Development

Abstract

There has been observed explosive growth in the development of mobile applications (apps) for Android and iOS operating systems, which has led to the direct impact towards mobile app development. In order to design and propose quality-oriented apps, it is the primary responsibility of developers to devote time and sufficient efforts towards testing to make the apps bug-free and operational in the hands of end-users without any hiccup. Manual testing procedures take a prolonged amount of time in writing test cases, and in some cases, the full testing requirements are not met. Besides, the insufficient knowledge of tester also impacts the overall quality and bug-free apps. To overcome the obstacles of testing, we propose a new testing methodology cum tool called “AgileUATM” which works primarily towards white-box and black-box testing. To evaluate the validity of the proposed tool, we put the tool in a real-time operational environment concerning mobile test apps. By using this tool, all the acceptance criteria are determined via user stories. The testers/developers specify requirements with formal specifications based on programs properties, predicates, invariants, and constraints. The results show that the proposed tool generated effective and accurate test cases, test input. Meanwhile, expected output was also generated in a unified fashion from the user stories to meet acceptance criteria. The proposed solution also reduced the development time to identify test data as compared to manual Behavior-Driven Development (BDD) methodologies. This tool can support the developers to get a better idea about the required tests and able to translate the customer’s natural languages to computer languages as well. This paper fulfills an approach to suitably test mobile application development.