Redundant test cases, although well-studied in software engineering, are previously underexplored in UI testing of mobile apps. Our study of real-world test suites shows that, equipped with large-scale testing suites, redundancy in UI testing often manifests as redundant UI interactions. Although negligible in traditional script-based workflows, such redundancy severely impacts the efficiency of emerging Large Language Model (LLM)-based UI agents, which incur substantial decision latency and token costs from repeated LLM queries for the same interactions. To this end, based on the idea of reusing LLMs’ former decisions, we present TestWeaver, a cost-effective LLM-based testing framework. Leveraging a semantic annotated UI Transition Graph (UTG), TestWeaver is capable of detecting shared interactions across test cases. It processes each interaction with a single LLM query, and reuses the result whenever the same interaction occurs. We evaluate TestWeaver on real-world test suites from Meituan. It achieves a 92% success rate with an average cost of $0.11 and 89.7 seconds per case, outperforming the state-of-the-art. We have also deployed TestWeaver in a real-world testing workflow at Meituan for over six months. TestWeaver has executed nearly 2,000 test cases and uncovered 10 previously undetected bugs, while reducing manual testing effort by 75%.

Lake extraction from remote sensing images is challenging due to the complex lake shapes and inherent data noises. Existing methods suffer from blurred segmentation boundaries and poor foreground modeling. This paper proposes a hybrid CNN-Transformer architecture, called LEFormer, for accurate lake extraction. LEFormer contains three main modules: CNN encoder, Transformer encoder, and cross-encoder fusion. The CNN encoder effectively recovers local spatial information and improves fine-scale details. Simultaneously, the Transformer encoder captures long-range dependencies between sequences of any length, allowing them to obtain global features and context information. The cross-encoder fusion module integrates the local and global features to improve mask prediction. Experimental results show that LEFormer consistently achieves state-of-the-art performance and efficiency on the Surface Water and the Qinghai-Tibet Plateau Lake datasets. Specifically, LEFormer achieves 90.86% and 97.42% mIoU on two datasets with a parameter count of 3.61M, respectively, while being 20× minor than the previous best lake extraction method. The source code is available at https://github.com/BastianChen/LEFormer.

Lake extraction from remote sensing imagery is a complex challenge due to the varied lake shapes and data noise. Current methods rely on multispectral image datasets, making it challenging to learn lake features accurately from pixel arrangements. This, in turn, affects model learning and the creation of accurate segmentation masks. This paper introduces a prompt-based dataset construction approach that provides approximate lake locations using point, box, and mask prompts. We also propose a two-stage prompt enhancement framework, LEPrompter, with prompt-based and prompt-free stages during training. The prompt-based stage employs a prompt encoder to extract prior information, integrating prompt tokens and image embedding through self- and cross-attention in the prompt decoder. Prompts are deactivated to ensure independence during inference, enabling automated lake extraction without introducing additional parameters and GFlops. Extensive experiments showcase performance improvements of our proposed approach compared to the previous state- of-the-art method. The source code is available at https://github.com/BastianChen/LEPrompter.

Optical satellite images are a critical data source; however, cloud cover often compromises their quality, hindering image applications and analysis. Consequently, effectively removing clouds from optical satellite images has emerged as a prominent research direction. Recent advances in deep learning-based cloud removal methods have been significant, but image generation quality still needs improvement. Diffusion models have demonstrated remarkable success in diverse image-generation tasks, showcasing their potential in addressing this challenge. This paper presents a novel framework called DiffCR, which leverages conditional guided diffusion with deep convolutional networks for high-performance cloud removal for optical satellite imagery. Specifically, we introduce a decoupled encoder for conditional image feature extraction, providing a robust color representation to ensure the close similarity of appearance information between the conditional input and the synthesized output. Moreover, we propose a novel and efficient time and condition fusion block within the cloud removal model to accurately simulate the correspondence between the appearance in the conditional image and the target image at a low computational cost. Extensive experimental evaluations on three commonly used benchmark datasets demonstrate that DiffCR consistently achieves state-of-the-art performance on all metrics, with parameter and computational complexities amounting to only 5.1% and 5.4%, respectively, of those previous best methods. The source code, pre-trained models, and all the experimental results will be publicly available at https://github.com/XavierJiezou/DiffCR upon the paper’s acceptance of this work.

In the face of increasing network traffic, network security issues have gained significant attention. Existing network intrusion detection models often improve the ability to distinguish network behaviors by optimizing the model structure, while ignoring the expressiveness of network traffic at the data level. Visual analysis of network behavior through representation learning can provide a new perspective for network intrusion detection. Unfortunately, representation learning based on machine learning and deep learning often suffer from scalability and interpretability limitations. In this article, we establish an interpretable multi-layer mapping model to enhance the expressiveness of network traffic data. Moreover, the unsupervised method is used to extract the internal distribution characteristics of the data before the model to enhance the data. What’s more, we analyze the feasibility of the proposed flow spectrum theory on the UNSW-NB15 dataset. Experimental results demonstrate that the flow spectrum exhibits significant advantages in characterizing network behavior compared to the original network traffic features, underscoring its practical application value. Finally, we conduct an application analysis using multiple datasets (CICIDS2017 and CICIDS2018), revealing the model’s strong universality and adaptability across different datasets.

Recent methods of cooperative multiagent rein-forcement learning built upon the individual global max value decomposition principle show promising results using variants of deep mixing networks, and credit assignment plays a crucial role in it. However, each agent in a multiagent system requires not only credit assignment but also credit feedback which tells each agent how many rewards it should obtain to maximize expected cumulative global rewards. In this work, we propose TLMIX, a novel Twin Leader Mixing Network for multiagent cooperation reinforcement learning while maintaining the centralized training and decentralized execution paradigm. TLMIX introduces a leader network to address the credit feedback issue by utilizing global information to provide reasonable objectives for agent networks. TLMIX also introduces a twin mixing network to find a more accurate target function from the Q-value functions, which avoids the rapid increase in parameter scale caused by introducing individual agents' twin networks and effectively mitigates the accumulation of high overestimation errors caused by temporal difference updates. Extensive results on SMAC experimental scenarios and the Predator-Prey environment demonstrate that TLMIX significantly outperforms comparable benchmark algorithms on convergence speed and performance.

In industrial practice, GUI (Graphic User Interface) testing of mobile apps still inevitably relies on huge manual efforts. The major efforts are those on understanding the GUIs, so that testing scripts can be written accordingly. Quality assurance could therefore be very labor-intensive, especially for modern commercial mobile apps, where one may include tremendous, diverse, and complex GUIs, e.g., those for placing orders of different commercial items. To reduce such human efforts, we propose Appaction, a learning-based automatic GUI interaction approach we developed for Meituan, one of the largest E-commerce providers with over 600 million users. Appaction can automatically analyze the target GUI and understand what each input of the GUI is about, so that corresponding valid inputs can be entered accordingly. To this end, Appaction adopts a multi-modal model to learn from human experiences in perceiving a GUI. This allows it to infer corresponding valid input events that can properly interact with the GUI. In this way, the target app can be effectively exercised. We present our experiences in Meituan on applying Appaction to popular commercial apps. We demonstrate the effectiveness of Appaction in GUI analysis, and it can perform correct interactions for numerous form pages.

Most methods for RGB-D salient object detection (SOD) utilize the same fusion strategy to explore the cross-modal complementary information at each level. However, this may ignore different feature contributions from two modalities on different levels towards prediction. In this paper, we propose a novel top-down multi-level fusion structure where different fusion strategies are utilized to effectively explore the low-level and high-level features. This is achieved by designing the interweave fusion module (IFM) to effectively integrate the global information and designing the gated select fusion module (GSFM) to discriminatively select useful local information by filtering out the unnecessary one from RGB and depth data. Moreover, we propose an adaptive fusion module (AFM) to reintegrate the fused cross-modal features of each level to predict a more accurate result. Comprehensive experiments on 7 challenging benchmark datasets demonstrate that our method achieves the competitive performance over 14 state-of-the-art RGB-D alternative methods.

In this work, we propose a novel light field fusion network-LFNet, a CNNs-based light field saliency model using 4D light field data containing abundant spatial and contextual information. The proposed method can reliably locate and identify salient objects even in a complex scene. Our LFNet contains a light field refinement module (LFRM) and a light field integration module (LFIM) which can fully refine and integrate focusness, depths and objectness cues from light field image. The LFRM learns the light field residual between light field and RGB images for refining features with useful light field cues, and then the LFIM weights each refined light field feature and learns spatial correlation between them to predict saliency maps. Our method can take full advantage of light field information and achieve excellent performance especially in complex scenes, e.g., similar foreground and background, multiple or transparent objects and low-contrast environment. Experiments show our method outperforms the state-of-the-art 2D, 3D and 4D methods across three light field datasets.