Writing UI tests manually requires significant effort. Several approaches have tried to address this problem in mobile apps: by exploiting the similarities of different apps within the same domain on a single platform, they have shown that it is possible to transfer tests that exercise similar functionality between the apps. A related recent technique enables transfer of UI tests uni-directionally, from an open-source iOS app to the same app implemented for Android. This paper presents MAPIT, a technique that expands existing work in three important ways: (1) it enables bi-directional UI test transfer between pairs of "sibling" Android and iOS apps; (2) it does not assume that the apps’ source code is available; (3) it is capable of transferring tests containing oracles in addition to UI events. MAPIT runs existing tests on a "source" app and builds a partial model of the app corresponding to each test. The model comprises the app’s screenshots, obtainable properties of each screenshot’s constituent elements, and labeled transitions between the screenshots. MAPIT uses this model to determine the corresponding information on the "target" app and generates an equivalent test, via a novel approach that leverages computer vision and NLP. Our evaluation on a diverse set of widely used, closed-source sibling Android and iOS apps shows that MAPIT is feasible, accurate, and useful in transferring UI tests across platforms.

The explosive increasing of vehicles cause amount of traffic problems. Although vehicle re-identification (Re-ID) can help to acquire and manage vehicles, some intrinsic difficulties hinder the application of vehicle Re-ID. For example, vehicles have little inter-instance discrepancy due to their rigid structures and finite models. To address this problem, in this paper, a logistic triplet loss is proposed to fuse a label-smoothing cross entropy to extract fine-grained feature embeddings. Via exploring deeper into the inter-instance variances, the novel loss combines advantages of classification and metric learning, and reveals more stable performance than popular triplet loss. The experimental results on public datasets demonstrate the effectiveness of the proposed loss compared with state-of-the-art approaches.

Person re-identification (Re-ID) is a challenging problem due to external environmental disturbances and significant intra-class appearance variations. Discriminative person features should cover global and partial representations, and accordingly can describe high-level and middle-level semantic information of persons. To achieve this goal, a saliency-based deep multi-level semantic feature representation and fusion algorithm is proposed. Firstly, a feature fusion scheme at middle layer of our deep network is presented to effectively fuse global CNN features. Secondly, a part-based feature extraction method is designed to extract high-level semantic features. In addition, a parameter-free multi-scale saliency-based enhancement algorithm is proposed to compute patch-level saliency scores to enhance the distinctiveness of part-based features. Experimental results on three public datasets, namely, Market-1501, DukeMTMC-reID, and CHUK03, demonstrate the effectiveness of the proposed method compared with state-of-the-art Re-ID approaches.

In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. The service quality matters because system failures could seriously affect business and user experience. A cloud service system typically contains a large number of computing nodes. In reality, nodes may fail and affect service availability. In this paper, we propose a failure prediction technique, which can predict the failure-proneness of a node in a cloud service system based on historical data, before node failure actually happens. The ability to predict faulty nodes enables the allocation and migration of virtual machines to the healthy nodes, therefore improving service availability. Predicting node failure in cloud service systems is challenging, because a node failure could be caused by a variety of reasons and reflected by many temporal and spatial signals. Furthermore, the failure data is highly imbalanced. To tackle these challenges, we propose MING, a novel technique that combines: 1) a LSTM model to incorporate the temporal data, 2) a Random Forest model to incorporate spatial data; 3) a ranking model that embeds the intermediate results of the two models as feature inputs and ranks the nodes by their failure-proneness, 4) a cost-sensitive function to identify the optimal threshold for selecting the faulty nodes. We evaluate our approach using real-world data collected from a cloud service system. The results confirm the effectiveness of the proposed approach. We have also successfully applied the proposed approach in real industrial practice.