Automated Test Input Generation Research Articles

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

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.

Quantifying the Exploration of the Korat Solver for Imperative Constraints

Tools that explore very large state spaces to nd bugs, e.g., when model checking, or to nd solutions, e.g., when constraint solving, can take a considerable amount of time before the search termi- nates, and the user may not get useful feedback on the state of the search during that time. Our focus is a tool that solves im- perative constraints to provide automated test input generation for systematic testing. Speci cally, we introduce a technique to quantify the exploration of Korat, a well-known tool that explores the bounded space of all candidate inputs and enumerates desired inputs that satisfy given constraints. Our technique quanti es the size of the input space as it is explored by the Korat search, and provides the user exact information on the size of the remaining input space. In addition, it allows studying key characteristics of the search, such as the distribution of solutions as the search nds them. We implement the technique as a listener for the Korat search, and present initial experimental results using it.

Crawl-based analysis of web applications: Prospects and challenges

In this paper we review five years of research in the field of automated crawling and testing of web applications. We describe the open source Crawljax tool, and the various extensions that have been proposed in order to address such issues as cross-browser compatibility testing, web application regression testing, and style sheet usage analysis.Based on that we identify the main challenges and future directions of crawl-based testing of web applications. In particular, we explore ways to reduce the exponential growth of the state space, as well as ways to involve the human tester in the loop, thus reconciling manual exploratory testing and automated test input generation. Finally, we sketch the future of crawl-based testing in the light of upcoming developments, such as the pervasive use of touch devices and mobile computing, and the increasing importance of cyber-security.