Summary The unmanned aircraft system (UAS) is rapidly gaining popularity in civil and military domains. A UAS consists of an application software that is responsible for defining a UAS mission and its expected behavior. A UAS during its mission experiences changes (or interruptions ) that require the unmanned aerial vehicle (UAV) in a UAS to self‐adapt, that is, to adjust both its behavior and position in real‐time, particularly for maintaining formation in the case of a UAS swarm. This adaptation is critical as the UAS operates in an open environment, interacting with humans, buildings, and neighboring UAVs. To verify if a UAS correctly makes an adaptation, it is important to test it. The current industrial practice for testing the self‐adaptive behaviors in UAS is to carry out testing activities manually. This is particularly true for existing UAS rather than newly developed ones. Manual testing is time‐consuming and allows the execution of a limited set of test cases. To address this problem, we propose an automated model‐based approach to test the self‐adaptive behavior of UAS application software. The work is conducted in collaboration with an industrial partner and demonstrated through a case study of UAS swarm formation flight application software. Further, the approach is verified on various self‐adaptive behaviors for three open‐source autopilots (i.e., Ardu‐Copter, Ardu‐Plane, and Quad‐Plane). Using the proposed model‐based testing approach we are able to test sixty unique self‐adaptive behaviors. The testing results show that around 80% of the behavior adaptations are correctly executed by UAS application software.

Abstract Model transformation testing has become crucial as model‐driven engineering has raised the abstraction level for developing software systems. Transformation is written to transform models from one level of abstraction to another, for example, model to model or model to code. A major challenge in testing the transformation is the creation of test models, such that (i) they conform to the source meta‐model (i.e., multiplicities and Object Constraint Language [OCL] constraints on meta‐model) and (ii) they provide coverage of the complete transformation (solving branch conditions for traversing all paths). Manual creation of test models requires a lot of time and effort. Still, the validity of the developed test models cannot be ensured. This paper aims to solve the above challenges using an automated search‐based strategy. The proposed approach is two‐stepped. First, valid test models are generated by solving source meta‐model constraints. Second, the generated models are evolved for achieving the structural coverage of the transformation by solving the branch conditions. A toolset model transformation testing environment (MOTTER) is developed to automate the search‐based solution. The proposed work is empirically evaluated on two case studies using four search algorithms. The result reflects that it successfully generates valid test models for achieving desired structural coverage with high performance on both the case studies.

Abstract Mobile 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.

Mobile application development has emerged as one of the most focused areas in software industry due to exponential growth of mobile users and applications. The focus of the current mobile industry is on direct implementation, rather than analysis and design. Model-driven engineering practices are ignored, which results in low reusability and lack of portability along with other challenges. In addition, mobile applications also have to support multiple platforms, as an application written for one platform (e.g., Android) cannot run on another platform (e.g., Windows Phone). Each of the platforms in turn suffers from fragmentation. This results in multiple versions of an application that need to be simultaneously maintained. This is a huge burden on the development team, both in terms of cost and effort, due to various platforms that an application supports. This paper proposes a model-driven approach to generate mobile applications for multiple platforms. We carefully identify a subset of UML and propose a modeling methodology for this purpose. We use real use-cases for requirement gathering, class diagram for structural modeling, while state machine are used for behavioral modeling. We focus on modeling business logic layer for the mobile application. We also propose a UML profile for modeling mobile domain specific concepts. Our approach uses Action Language for Foundational Subset of UML (ALF) to specify actions in the state machines. Our approach allows the modeler to automatically generate business logic code for multiple platforms. The approach is supported by an automated tool MAG (Mobile Application Generator). We apply our model-driven approach on an industrial case study 'Scramble' app developed by 'Next In' to demonstrate its viability.

This paper presents a methodology for identifying changes and test case selection based on the UML designs of the system. Design artifacts used for this purpose are UML class diagram and sequence diagrams, which are used to generate an extended concurrent control flow graph (ECCFG) which is further used for regression testing, i.e., change identification and test case selection. A proof- of-concept tool has been developed and used on a case study, which shows that our approach selects a precise set of test cases from an existing test suite.