Ever since its initial release in 2008, the Android OS has rapidly grown to become the world’s most widely used mobile OS. Mobile vendors extend the Android Open Source Project (AOSP) led by Google to customize their own Android variants. With the AOSP releasing new versions frequently, vendors need to periodically carry out Android upgrades that integrate the latest code changes from AOSP into their Android variants. Both the AOSP and Android variants independently undergo complex modifications. Consequently, Android upgrades often lead to merge conflicts caused by competing changes to the same code line. Vendors have devoted significant effort to understanding and resolving these problems. Despite extensive research on Android upgrades and merge conflicts, there is little understanding of the conflict-related activities performed in Android upgrade practice, and the corresponding challenges faced by practitioners . In this study, we employed a qualitative research methodology involving questionnaires with 120 practitioners and interviews with a leading Android vendor to explore the challenges and improvement opportunities. Our investigation demonstrates that the Android upgrade process is fundamentally an exercise in architectural evolution, necessitating the adoption of architectural thinking rather than relying on mere code-level patches to systematically address upgrade-induced challenges. We have identified challenges at different stages of Android upgrade implementation, including baseline analysis, conflict reason analysis, conflict resolution, and conflict impact analysis. Our findings indicate opportunities for enhancing the Android upgrade practice, particularly in documentation, management, refactoring activities, and team collaboration. Additionally, we outline future research directions from an architectural perspective. We envision that our study can benefit software ecosystems where customized downstream derivatives need to maintain co-evolution with their upstream core.

As software continues to evolve and business functions become increasingly complex, architectural inconsistency arises when the implementation architecture deviates from the expected architecture design. This architectural problem makes maintenance difficult and requires significant effort to refactor. To assist labor-intensive refactoring, automated refactoring has received much attention such as searching for optimal refactoring solutions. However, there are still three limitations: The recommended refactorings are insufficiently effective in addressing architectural consistency; the search process for refactoring solution is inefficient; and there is a lack of executable refactoring solutions. To address these limitations, we propose an effective, efficient, and executable refactoring recommendation approach namely the 3Erefactor for software architectural consistency. To achieve effective refactoring, 3Erefactor uses NSGA-II to generate refactoring solutions that minimize architectural inconsistencies at module level and entity level. To achieve efficient refactoring, 3Erefactor leverages architecture recovery technique to locate files requiring refactoring, helping accelerate the convergence of refactoring algorithm. To achieve executable refactoring, 3Erefactor designs a set of refactoring executability constraint strategies during the refactoring solution search and generation, including improving refactoring pre-conditions and removing invalid operations in refactoring solutions. We evaluated our approach on six open source systems. Statistical analysis of our experiments shows that, the refactoring solution generated by 3Erefactor performed significantly better than 3 state-of-the-art approaches in terms of reducing the number of architectural inconsistencies, improving the efficiency of the refactoring algorithm and improving the executability of refactorings.

Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility. Yet their potential has not been fully exploited, where the extra degree of freedom in OMR can potentially enable the robot to carry out extra tasks. For instance, gimbals or sensors on robots may suffer from a limited field of view or be constrained by the inherent mechanical design, which will require the chassis to be orientation-aware and respond in time. To solve this problem and further develop the OMR systems, in this paper, we categorize the tasks related to OMR chassis into orientation transition tasks and position transition tasks, where the two tasks can be carried out at the same time. By integrating the parallel task goals in a single planning problem, we proposed an orientation-aware planning architecture for OMR systems to execute the orientation transition and position transition in a unified and efficient way. A modified trajectory optimization method called orientation-aware timed-elastic-band (OATEB) is introduced to generate the trajectory that satisfies the requirements of both tasks. Experiments in both 2D simulated environments and real scenes are carried out. A four-wheeled OMR is deployed to conduct the real scene experiment and the results demonstrate that the proposed method is capable of simultaneously executing parallel tasks and is applicable to real-life scenarios.