Phone vendors customize their Android variants to enhance system functionalities based on the Android Open Source Project (AOSP). While independent development, Android variants have to periodically evolve with the upstream AOSP and merge code changes from AOSP. Vendors have invested great effort to maintain their variants and resolve merging conflicts. In this paper, we characterize the design smells with recurring patterns that break the design boundary between Android variants and AOSP. These smells are manifested as problematic dependencies across the boundary, hindering Android variants' maintainability and co-evolution with AOSP. We propose the DroidDS for automatically detecting design smells. We collect 22 Android variant versions and 22 corresponding AOSP versions, involving 4 open-source projects and 1 industrial project. Our results demonstrate that: files involved in design smells consume higher maintenance costs than other files; these infected files are not merely the files with large code size, increased complexity, and object-oriented smells; the infected files have been involved in more than half of code conflicts induced by re-applying AOSP's changes to Android variants; a substantial portion of design issues could be mitigable. Practitioners can utilize our DroidDS to pinpoint and prioritize design problems for Android variants. Refactoring these problems will help keep a healthy coupling between diverse variants and AOSP, potentially improving maintainability and reducing conflict risks.

ABSTRACT Distributed manufacturing has become a mainstream production mode in economic globalization. A discrete gray wolf optimizer (DGWO) is proposed to solve the distributed permutation flowshop scheduling problem (DPFSP) to minimize makespan. First, an extended Nawaz‐Enscore‐Ham2 (ENEH2) and a randomly generated hybrid initialization method are used to enhance the diversity and ergodicity of the population. Second, a discrete population update mechanism is proposed for the characteristics of the solved problem to balance exploration and exploitation. The variable neighborhood descent search strategy is used to further improve the quality of the solution. Finally, the Wilcoxon signed rank and the Friedman test are used for statistical comparison analysis. To verify the performance of the DGWO, simulation experiments are conducted on different scales of instances and compared with various methods to demonstrate the advantages of the DGWO for solving the DPFSP.

Summary The permutation flow shop scheduling problem, as a classical problem in the scheduling field, is an NP‐hard problem. However, most of the reported algorithms are difficult to achieve good accuracy and efficiency. To address this problem, a hybrid grey wolf optimizer (HGWO) is proposed in this paper. First, one cooperative initialization strategy is proposed to improve the quality of the initial solution based on the improved Nawaz‐Enscore‐Ham (NEH) method and the tent chaotic map method. Second, a levy flight strategy is introduced to balance the exploitation and exploration of the algorithm for the problem's characteristics. Third, the crossover and mutation strategy, and the critical block exchange based on critical path strategy are proposed to avoid falling into the local optimum. In addition, for the best individual, the variable neighborhood descent strategy is proposed to enhance the convergence accuracy of the algorithm. To verify the performance of the proposed algorithm, three different types of instances are selected for comparison experiments with other existing methods, and the experimental results show that the proposed HGWO outperforms other comparison algorithms in solving the problem.

Graph neural networks (GNNs) are playing exciting roles in the application scenarios where features are hidden in information associations. Fraud prediction of online credit loan services (OCLSs) is such a typical scenario. But it has another rather critical challenge, i.e., the scarcity of data labels. Fortunately, GNNs can also cope with this problem due to their good ability of semi-supervised learning by mining structure and feature information within graphs. Nevertheless, the gain of internal information is often too limited to help GNNs handle the extreme deficiency of labels with high performance beyond the basic requirement of fraud prediction in OCLSs. Therefore, adding labels from the experts, such as manually adding labels through rules, has become a logical practice. However, the existing rule engines for OCLSs have the confliction problem among continuously accumulated rules. To address this issue, we propose a Snorkel-based Semi-Supervised GNN (S3GNN). Under S3GNN, we specially design an upgraded version of the rule engines, called Graph-Oriented Snorkel (GOS), a graph-specific extension of Snorkel, a widely used weakly supervised learning framework, to design rules by subject matter experts (SMEs) and resolve confliction. In particular, in the graph of an anti-fraud scenario, each node pair may have multiple different types of edges, so we propose the Multiple Edge-Types Based Attention mechanism. In general, for the heterogeneous information and multiple relations in the graph, we first obtain the embedding of applicant nodes by aggregating the representation of attribute nodes, and then use the attention mechanism to aggregate neighbor nodes on multiple meta-paths to get ultimate applicant node embedding. We conduct experiments over the real-life data of a large financial platform. The results demonstrate that S3GNN can outperform the state-of-the-art methods, including the method of pilot platform.

Mobile device vendors develop their customized Android OS (termed downstream) based on Google Android (termed upstream) to support new features. During daily independent development, the downstream also periodically merges changes of a new release from the upstream into its development branches, keeping in sync with the upstream. Due to a large number of commits to be merged, heavy code conflicts would be reported if auto-merge operations failed. Prior work has studied conflicts in this scenario. However, it is still unclear about the coupling between the downstream and the upstream (We term this coupling as the dependency facade), as well as how merge conflicts are related to this coupling. To address this issue, we first propose the DepFCD to reveal the dependency facade from three aspects, including interface-level dependencies that indicate a clear design boundary, intrusion-level dependencies which blur the boundary, and dependency constraints imposed by the upstream non-SDK restrictions. We then empirically investigate these three aspects (RQ1, RQ2, RQ3) and merge conflicts (RQ4) on the dependency facade. To support the study, we collect four open-source downstream projects and one industrial project, with 15 downstream and 15 corresponding upstream versions. Our study reveals interesting observations and suggests earlier mitigation of merge conflicts through a well-managed dependency facade. Our study will benefit the research about the coupling between upstream and downstream as well as the downstream maintenance practice.

Posterior probability support vector machines (PPSVMs) prove robust against noises and outliers and need fewer storage support vectors (SVs). Gonen et al. (2008) extended PPSVMs to a multiclass case by both single-machine and multimachine approaches. However, these extensions suffer from low classification efficiency, high computational burden, and more importantly, unclassifiable regions. To achieve higher classification efficiency and accuracy with fewer SVs, a binary tree of PPSVMs for the multiclass classification problem is proposed in this letter. Moreover, a Fisher ratio separability measure is adopted to determine the tree structure. Several experiments on handwritten recognition datasets are included to illustrate the proposed approach. Specifically, the Fisher ratio separability accelerated binary tree of PPSVMs obtains overall test accuracy, if not higher than, at least comparable to those of other multiclass algorithms, while using significantly fewer SVs and much less test time.