Zheheng Liang

China Southern Power Grid (China)

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Co-Authors

Yechao Wang

China Southern Power Grid (China)

Zhenyue Long

China Southern Power Grid (China)

Wuqiang Shen

China Southern Power Grid (China)

Lei Cui

China Southern Power Grid (China)

Xiaolu Zhang

China Southern Power Grid (China)

Wuqiang Shen

China Southern Power Grid (China)

Chaosheng Yao

China Southern Power Grid (China)

Zhenyue Long

China Southern Power Grid (China)

Peng Yue

Wuhan University

Guiquan Shen

Affiliated Hospital of Guizhou Medical University

Cooperative Institutions

China Southern Power Grid (China)

139

Chinese Academy of Sciences

University of Chinese Academy of Sciences

Institute of Software

Wuhan University

State Key Laboratory of Computer Science

Delhi Technological University

Sun Yat-sen University

Power Grid Corporation (India)

Koneru Lakshmaiah Education Foundation

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Characterizing Flaky Tests in Node.js Applications

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) (2023)

Xiaoning Chang Zheheng Liang Guoquan Wu Yu Gao Wei Chen

Regression testing is an important means of assessing the quality of Node.js applications. However, non-deterministic executions inside Node.js framework could make test cases intermittently pass or fail on the same version of code, which are called flaky tests. Flaky tests can cause unreliable test results, and make developers waste a significant amount of time debugging the bugs that do not belong to the target application. In this paper, we conduct an empirical study on 87 flaky tests from 7 popular Node.js applications, and analyze the non-determinism that causes these flaky tests. Through this study, there is a wide range of non-determinism to cause flaky tests, including non-deterministic event triggering order, non-deterministic function calls, non-deterministic process/thread scheduling order, non-deterministic execution of asynchronous tasks and non-deterministic event triggering data. The result reveals that, existing approaches on event race detection are not sufficient for flaky test detection. In future, researchers can design flaky test detection approaches targeted at different categories of non-determinism.

Regression testing

Code coverage

10.1109/ase56229.2023.00025

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A Dynamic Multi-Branch Neural Network Module for 3D Point Cloud Classification and Segmentation Using Structural Re-parametertization

Shunhe Yu Ming Wang Chenxiao Zhang Li J Kai Yan

3D point cloud is an important geometric data structure. In recent years, deep learning for 3D point cloud has attracted more and more attention and has been widely applied in autonomous driving, robotics and some other fields. Existing 3D point cloud deep learning models can guarantee a relatively high accuracy during training while sacrificing the inference speed due to the large amount of model parameters, which poses a challenge for real-time semantic segmentation of 3D point clouds. To accelerate the inference process, alternative solutions optimize the model through model pruning (reducing the amount of parameters and calculations of the model) or model quantization (compressing the amount of data size), which will inevitably lead to loss of accuracy. Therefore, a natural thought is to balance inference speed and training accuracy. In the paper, we propose a plug-and-play dynamic multi-branch neural network module, which can improve the training accuracy without pruning the size of the inference model or hampering the speed of inference. This module refers to the idea of structural re-parameterization, which we named it Rep-PointNet Module. In general, Rep-PointNet Module can efficiently improve the training accuracy of the model while performing lossless compression of the model. The experiment results show that our method beats the state-of-the-art on the ShapeNet dataset with rapid inference speed. The gains in the overall accuracy metric brought by the Rep-PointNet Module are about 1.5% and 2% on the classification task and the segmentation task, respectively.

Pruning

10.1109/agro-geoinformatics59224.2023.10233616

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Research on Intelligent Concentrator Based on Edge Computing Technology

Xiaojiang Chen Tinghui Lu Zeng Jijun Zheheng Liang Fengchan Guo

At present, the power metering equipment used at the grass-level sends the collected data to the intelligent concentrator, and then it is stored and forwarded to the master station through the concentrator, and finally the data is processed through cloud computing of the main station. Because the concentrator currently used cannot process data, it brings great trouble to the system master station. Aiming at this situation, this paper proposes the design method of concentrator based on edge computing technology. Firstly, the edge computing technology is introduced briefly, and then the problems of current concentrator are improved, and a new generation of intelligent concentrator is proposed. Finally, the system function is designed as a whole, and the hardware architecture and communication scheme are given. The system can not only solve the problem of data processing, but also has real-time monitoring and secure and reliable communication.

Concentrator

Data Processing

10.1109/ccdc49329.2020.9163960

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Research on Template Discovery Technology for Multi-Source Heterogeneous Log Fusion

Wuqiang Shen Zheheng Liang Yechao Wang Lei Cui Chaosheng Yao

10.1109/ecnct63103.2024.10704267

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砂箱技術に基づくオペレーティングシステムとハードウェアストリッピングのシミュレーション【JST・京大機械翻訳】

Advances in intelligent systems and computing (2020)

Zheheng Liang Zhang Xiaolu Jiang Daohuan Wuqiang Shen Li Hao

Source

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The application of illegal building detection from VHR UAV remote sensing images based on convolutional neural network

Bulletin of Surveying and Mapping (2021)

Zheheng Liang Peng Deng Fuquan Jiang Sheng Sen Wei Rulan

10.13474/j.cnki.11-2246.2021.0120

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Citations (2)

Table of Contents

Rong Xu Hui Liu Haoxiang Wang Shivakumar Kagi Basavaraj Mathapati

Table (database)

10.1109/icict50816.2021.9358652

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A Software Test Metric Method Based on Fuzzy Logic

DEStech Transactions on Computer Science and Engineering (2019)

Daohuan Jiang Zheheng Liang Dexin Huang

Software test metric is a quantitative analysis in the software test processes. This paper introduces the method that applies fuzzy logic in software test metric, making software test metric more accurate and efficient. This method can increase liability and accuracy in software test and it is regarded as a practical software metric test.

Software metric

10.12783/dtcse/iciti2018/29147

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GraphFlow: A Fast and Accurate Distributed Streaming Graph Computation Model

Zheheng Liang Yingying Zheng Shengli Bi Chaosheng Yao Jiayan Wang

10.1109/icpads63350.2024.00039

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Search-Based Performance Testing and Analysis for Microservice-Based Digital Power Applications

2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE) (2023)

Qiugen Pei Zheheng Liang Zeling Wang Lei Cui Zhenyue Long

Modern digital power applications (e.g., power trading system) are moving toward microservice architecture, in which each service is developed and managed independently, and new features and updates are delivered continuously. It is essential to manifest the performance problem of the application early and often, before the code ships to the production. In this paper, we propose a novel performance testing and analysis approach for microservice based application, which first designs a multi-objective search technique to expose the performance problem, and then detects potential performance bottleneck by aggregating and comparing normal/abnormal traces. The evaluation results on an open source microservice benchmark and an industrial digital power application show that our approach is effective, and can expose performance anomaly and detect performance bottleneck effectively.

Benchmark (surveying)

Microservices

10.1109/ceepe58418.2023.10165808

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Citations (1)