An automated model‐based approach for unit‐level performance test generation of mobile applications

AbstractAbstractMobile devices have limited resources, including memory and processing speed. The performance of mobile applications is an important concern. There are a large number of mobile platforms available with varying operating systems and hardware. Native applications are usually developed and maintained separately for these platforms. The overall performance of native applications may significantly vary across platforms. The current industrial practice is to manually test the performance for each variant, which is not a scalable or efficient approach. We tackled the problem of generating native application variants in our previous work. This paper proposes an automated model‐based approach for performance test generation for native application variants at unit level. We propose a performance profile that allows modeling of domain‐specific performance parameters on UML models, which are used for automated performance test generation for each native variant. The results of applying the approach on two real‐world applications show that the approach evaluates the performance of application variants for two different versions of Android successfully and have potential to reduce the effort and time. A questionnaire‐based study is conducted to evaluate the usefulness of the approach.This paper proposes an automated model‐based approach for performance test generation for native application variants at unit‐level that specifically focuses on the evaluation of mobile device processing, memory, and battery consumption. The contributions of the paper includes (1) a performance modeling profile (PerMP) that allows modeling of mobile domain‐specific performance characteristics, (2) an automated performance test generation strategy that allows performance testing across platforms, (3) evaluation of the proposed strategy by applying on the industrial case studies, and (4) a questionnaire study to evaluate the usefulness of the approach. The results show that the approach evaluates the performance of application variants for two versions of Android platform successfully and have potential to reduce the effort and time. View Figure