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Proceedings - 20th IEEE/ACIS International Summer Conference on computer and Information Science, ICIS 2021-Summer 2021年 X页

作者： Gong, Jiayu Shanghai Key Laboratory of Computer Software Testing and Evaluating China

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SQDroid: A Semantic-Driven testing for Android Apps via Q-learning

SQDroid: A Semantic-Driven Testing for Android Apps via Q-le...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Hui Guo Xiaoqiang Liu Baiyan Li Lizhi Cai Yun Hu Jing Cao School of Computer Science and Technology Donghua University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing ShanghaiChina

ISBN: (纸本)9781665458146

Android apps are popular in our daily life, while the testing and maintenance of them is still an open challenge. Random-based testing tools are time-consuming and model-based testing tools are unrealistic to construct all functional behaviors precisely. Existing testing tools based on reinforcement learning not only have difficulty in understanding the business logic of an application, but also face the problem of state explosion during testing. In this paper, we propose SQDroid, a semantic-driven approach for Android apps based Q-learning. SQDroid encourages the Q-learning agent to prefer the actions with functional semantics through a dynamic semantic reward function, which is beneficial to the understand business logic of an app. A state clustering module, employed to compress the state space in Q-table, utilizes the widget attributes on GUI hierarchy to abstract a state. It can reduce the number of states in Q-table and avoid the execution of repetitive actions. We evaluate SQDroid on 48 open-source Android apps. The results show SQDroid outperforms the state-of-the-art model-based/search-based technique/reinforcement learning-based Stoat, Sapienz and Q-testing in terms of code coverage and fault revelation.

关键词： Q-learning Semantics software quality Maintenance engineering software reliability Security Open source software

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M-Detr: Multi-Scale Detr for Optical Music Recognition

SSRN

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SSRN 2023年

作者： Luo, Fei Dai, Yifan Fuentes, Joel Ding, Weichao Zhang, Xueqin East China University of Science and Technology Shanghai China Department of Computer Science and Information Technologies Universidad del Bío-Bío Chillán3780000 Chile Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

As a new paradigm for object detection, DETR (detection transformer) has the ability to associate contextual information, which is very suitable for optical music recognition (OMR). This paper proposes a novel multi-scale detection transformer (M-DETR) for OMR with a multi-scale feature fusion mechanism and improved attention mechanisms. First, the feature fusion mechanism is combined with backbone networks to let the M-DETR network get richer multi-scale information. Then, a key-region attention mechanism is incorporated based on the character that the key information in a score image. Finally, the pre-context attention mechanism enables the network to make better use of the contextual association between recognition targets in music scores. Experiment results show that M-DETR achieves recognition accuracy of 90.6% for 7 typical small-sized notes, which is better than Faster R-CNN and YOLO v5, and the improvement rate is 10.02% compared to the original DETR algorithm. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Automatic Collaborative testing of Applications Integrating Text Features and Priority Experience Replay

Automatic Collaborative Testing of Applications Integrating ...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Lizhi Cai Jin Wang Mingang Chen Jilong Wang School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

With the popularity of deep reinforcement learning(DRL), people have great interest in using deep reinforcement learning for application automated testing. However, most automated testing methods based on reinforcement learning ignore text information, use random sampling in experience replay and ignore the characteristics of Android automated testing. To solve above problem, this paper proposes ITPRtesting(Integrated Text feature information and Priority experience in testing). It extracts the text information in the interface and uses the BERT algorithm to generate sentence vectors. It fuses the interactive control feature diagram(ICFD), which is mentioned in the previous work, and text information as the state required by reinforcement learning. And in reinforcement learning, the priority experience replay is combined, also the traditional priority experience replay is improved. This paper has carried out experiments on 10 open source applications. The experimental results show that ITPRtesting is superior to other methods in statement coverage and branch coverage.

关键词： Deep learning Fuses Bit error rate Reinforcement learning software quality Feature extraction software reliability

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Research on Quality Model for Quantum Simulator software

Research on Quality Model for Quantum Simulator Software

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software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), ACIS International Winter Conference on

作者： Chao Wang Jingwei Chen JianXin Ge XueZhong Wu QianQian Qu Le Luo Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China QUDOOR Technologies Inc. Hefei China Software Engineering of Computer and Information Engineering College Shanghai Polytechnic University Shanghai China QUDOOR Technologies Inc. School of Physics and Astronomy Sun Yat-Sen University Zhuhai China

In recent years, the demand for arithmetic power from all walks of life is growing, especially the increasing popularity of AIGC, which makes people realize that huge technological progress cannot be made without the support of large arithmetic power. Quantum computing may bring a leap in computing power and become a ”trigger” and “catalyst” to accelerate the development of science and technology innovation, and its technical development trend and application prospects have become a hot issue of concern, and quantum simulators are a very important part of the development process. The development of quantum simulators is a very important part of the process. However, due to the lack of systematic evaluation theories and methods, it is difficult to quantify and analyze the quality of the increasing number of simulators on the market, which may hinder researchers from practicing their quantum algorithms objectively or developing quantum technologies. For these reasons, this paper proposes a quality model for quantum simulator software, which reflects the quality characteristics of simulators in seven aspects, such as functionality and performance efficiency, and selects some simulators for experimental validation of the proposed model to prove its rationality.

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Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

Joint Clustering and Analyze Single Cell Multi-omics Data by...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Congcong An Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore SG Faculty of Engineering The University of Sydney Australia

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data.

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Multi-Memory Matching for Unsupervised Visible-Infrared Person Re-Identification

arXiv

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arXiv 2024年

作者： Shi, Jiangming Yin, Xiangbo Chen, Yeyun Zhang, Yachao Zhang, Zhizhong Xie, Yuan Qu, Yanyun Institute of Artificial Intelligence Xiamen University Xiamen China School of Informatics Xiamen University Xiamen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Unsupervised visible-infrared person re-identification (USL-VI-ReID) is a promising yet highly challenging retrieval task. The key challenges in USL-VI-ReID are to accurately generate pseudo-labels and establish pseudo-label correspondences across modalities without relying on any prior annotations. Recently, clustered pseudo-label methods have gained more attention in USL-VI-ReID. However, most existing methods don’t fully exploit the intra-class nuances, as they simply utilize a single memory that represents an identity to establish cross-modality correspondences, resulting in noisy cross-modality correspondences. To address the problem, we propose a Multi-Memory Matching (MMM) framework for USL-VI-ReID. We first design a simple yet effective Cross-Modality Clustering (CMC) module to generate the pseudo-labels through clustering together both two modality samples. To associate cross-modality clustered pseudo-labels, we design a Multi-Memory Learning and Matching (MMLM) module, ensuring that optimization explicitly focuses on the nuances of individual perspectives and establishes reliable cross-modality correspondences. Finally, we design a Soft Cluster-level Alignment (SCA) loss to narrow the modality gap while mitigating the effect of noisy pseudo-labels through a soft many-to-many alignment strategy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the reliability of the established cross-modality correspondences and the effectiveness of MMM. © 2024, CC BY.

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wen Zhu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything" to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA’s LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS’s unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes.

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Test case generation for web application based on markov reward process

Test case generation for web application based on markov rew...

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2020 International Conference on Communications, Information System and software Engineering, CISSE 2020

作者： Cao, Jing Liu, Xiaoqiang Guo, Hui Cai, Lizhi Hu, Yun College of Computer Science and Technology Donghua University Shanghai201620 China Shanghai Key Laboratory of Computer Software Testing &Evaluation Shanghai201112 China

Web applications often face continuous updating due to functional change or UI renew, while it remains a challenge to guarantee their correctness. The goal of software testing is to find defects in a limited time range whereas exhaustive testing is an ideal yet time-consuming process. In this research, we propose an approach to generating test cases automatically based on the Markov reward process which innovatively contains a reward function for test results to guide the generation of test cases. By using the N-step algorithm, this approach can generate the test flow with the highest risk priority which can capture software defects as quickly as possible. The experiment on an e-commerce system shows that there is significant improvement on the defect detection capability of test cases generated through Markov reward process. © 2021 Journal of Physics: Conference Series.

关键词： software testing

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