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9th IEEE Smart World Congress, SWC 2023

作者： Li, Huacheng Xia, Chunhe Wang, Tianbo Zhao, Haopeng Beihang University Key Laboratory of Beijing Network Technology Beijing China Guangxi Collab. Innov. Ctr. of Multi-Src. Info. Intgn. and Intelligent Proc. Guangxi Norm. Univ. Guilin China Beihang University School of Cyber Science and Technology Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9798350319804

Understanding the information diffusion trajectory in online social networks has become an ever-growing prominent research direction in the information era. Users interact with social information they are interested in, and cascades record diffusion trajectories of massive social information. It implies two characteristics of diffusion cascades: topic sharing among different cascades and infected user correlation within the same cascades. However, previous works ignore these two characteristics and primarily focus on whether each infected user is correctly predicted individually, which leads to learning biased diffusion models under the data-driven mode. To address this problem, we propose a novel architecture to exploit such latent user associations among topic-related cascades. First, we leverage the hypergraph and heterogeneous graph to exploit infected user correlations jointly and generate user feature vectors. Second, we devise an enhanced cascade similarity loss function to evaluate prediction results from delay (horizontal) and rank (vertical) scopes. Furthermore, we introduce the extra predictor to alleviate the interference with the primary model. Experiments on four public datasets validate the effectiveness of our proposed method and significantly outperform existing models on hits@κ and map@κ indicators. © 2023 IEEE.

关键词： heterogeneous hyper graph information diffusion prediction loss function social network analysis

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Attribute-Based Non-Interactive Privacy-Protected Crowd-Sourcing software testing Engineer Selection Mechanism

Attribute-Based Non-Interactive Privacy-Protected Crowd-Sour...

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

作者： Qiliang Yang Lizhi Cai Jiayu Gong Minlu Tang Yi Lin Shanghai Development Center of Computer Software Technology Cryptographic Standard Study & Security Evaluation Laboratory Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

The central management platforms in traditional crowd-sourced software testing networks are easy to become the target of attack by the enemies, and when the central management platform is attacked, it is easy to cause a single point of failure and bring the whole system down. In addition, when selecting software testing engineers for crowd-sourcing software testing tasks, most mechanisms select engineers based on their credits. This approach is too one-sided and fails to achieve fine-grained engineer selection. In order to solve above problems, we have proposed a non-interactive and fine-grained crowd-sourcing software testing engineer selection mechanism. In our mechanism, the task publisher can select engineers according to their attributes. Next, we have given an implementation of our non-interactive software testing engineer selection mechanism by applying QAP. The privacy of task publisher and engineers is protected. The security proof is also given. Simulation results show that our mechanism is feasible.

关键词： software testing Privacy Simulation software quality Reliability engineering software reliability Security

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Detection and Segmentation of Graphical Elements on GUIs for Mobile Apps Based on Deep Learning 11th

Detection and Segmentation of Graphical Elements on GUIs for...

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11th International Conference on Mobile Computing, Applications, and Services, MobiCASE 2020

作者： Hu, Rui Chen, Mingang Cai, Lizhi Chen, Wenjie Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9783030642136

Recently, mobile devices are more popular than computers. However, mobile apps are not as thoroughly tested as desktop ones, especially for graphical user interface (GUI). In this paper, we study the detection and segmentation of graphical elements on GUIs for mobile apps based on deep learning. It is the preliminary work of GUI testing for mobile apps based on artificial intelligence. We create a dataset, which consists of 2,100 GUI screenshots (or pages) labeled with 42,156 graphic elements in 8 classes. Based on our dataset, we adopt Mask R-CNN to train the detection and segmentation of graphic elements on GUI screenshots. The experimental results show that the mAP value achieves 98%. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Graphical user interfaces

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Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic Consistency

Multi-Signal Fusion of Social Diffusion Graph with Bi-Direct...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huacheng Li Chunhe Xia Tianbo Wang Wanshuang Lin Changnan Jiang Chen Chen Yuan Zhao Key Laboratory of Beijing Network Technology Beihang University Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University School of Cyber Science and Technology Beihang University Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

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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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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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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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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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An empirical study on the impact of class overlap in just-in-time software defect prediction 33

An empirical study on the impact of class overlap in just-in...

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33rd International Conference on software Engineering and Knowledge Engineering, SEKE 2021

作者： Yi, Minyang Fan, Guisheng Yu, Huiqun Yang, Xingguang Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China

ISBN: (纸本)1891706527

Just-in-time software defect prediction (JIT-SDP) is an active research topic in the field of software engineering, aiming at identifying defect-inducing code changes. Most of the current JIT-SDP work focused on model construction. It is often ignored that the performance of classifiers often depends on high quality data. In this paper, we first investigate the impact of the class overlap problem on the performance of the classifiers in JIT-SDP, and propose a new effective preprocessing method (IKMCCA-TL) combining improved K-Means clustering cleaning approach and Tomek-link method. In order to objectively estimate the impact of class overlap on the classifiers in JIT-SDP, we conduct a large-scale empirical study on the data sets of six open source projects and compare the performance of LR, RF and KNN classifiers by using IKMCCA or KMCCA or NCL and without cleaning data. Experimental results show that after removing overlapping instances, the performance of the classifiers is significantly improved in terms of balance, recall and AUC and our proposed method achieves the best performance. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： K-means clustering

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Instance and Category Supervision are Alternate Learners for Continual Learning

Instance and Category Supervision are Alternate Learners for...

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International Conference on computer Vision (ICCV)

作者： Xudong Tian Zhizhong Zhang Xin Tan Jun Liu Chengjie Wang Yanyun Qu Guannan Jiang Yuan Xie East China Normal University Shanghai Key Laboratory of Computer Software Testing & Evaluating Chongqing Institute of East China Normal University Tencent YouTu Lab Xiamen University Contemporary Amperex Technology Co. Limited

Continual Learning (CL) is the constant development of complex behaviors by building upon previously acquired skills. Yet, current CL algorithms tend to incur class-level forgetting as the label information is often quickly overwritten by new knowledge. This motivates attempts to mine instance-level discrimination by resorting to recent self-supervised learning (SSL) techniques. However, previous works have pointed out that the self-supervised learning objective is essentially a trade-off between invariance to distortion and preserving sample information, which seriously hinders the unleashing of instance-level *** this work, we reformulate SSL from the information-theoretic perspective by disentangling the goal of instance-level discrimination, and tackle the trade-off to promote compact representations with maximally preserved invariance to distortion. On this basis, we develop a novel alternate learning paradigm to enjoy the complementary merits of instance-level and category-level supervision, which yields improved robustness against forgetting and better adaptation to each task. To verify the proposed method, we conduct extensive experiments on four different benchmarks using both class-incremental and task-incremental settings, where the leap in performance and thorough ablation studies demonstrate the efficacy and efficiency of our modeling strategy.

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