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An Implementation of Centralized Controllers for software-Defined Satellite Networks 2

An Implementation of Centralized Controllers for Software-De...

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2nd asia-pacific conference on Communications Technology and Computer Science, ACCTCS 2022

作者： Ma, Yuke Li, Jing Huang, Yunxue Information and Communication Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665400343

With the increasing user demands for higher quality multimedia services, the satellite networks play a vital role in communication networks due to global seamless coverage. However, the time-variant topologies of satellite networks bring great challenges to network management and routing algorithms. This paper aims at applying the software defined networking (SDN) to satellite networks to improve routing calculation efficiency and simplify satellite payloads. Firstly, we obtain the satellite network topologies from a satellite simulation tool and build the satellite network system based on the Iridium constellation. Moreover, the dynamic satellite network topology is divided into multiple static network topologies by dividing time slices. Finally, to get the optimal path, we develop the routing algorithm using the improved Dijkstra algorithm to dynamically establish an end-to-end communication path under the TCP connections, so that the communication between satellites can be achieved. © 2022 IEEE.

关键词： Network topology

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Effective Bug Triage Based on a Hybrid Neural Network 28

Effective Bug Triage Based on a Hybrid Neural Network

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28th asia-pacific software engineering conference (APSEC)

作者： Wang, Hongbing Li, Qi Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9781665437844

With the increasing scale and complexity of open source software, the quality of software has become a focus to which repairers pay close attention. Due to the inevitable existence of some known or unknown bugs in software,under certain conditions, software bugs may directly cause program running errors, and then produce abnormal running results and wrong program behavior, which will cause huge economic losses. Therefore, software defect repair is an important part of software evolution and quality assurance. Quickly and efficiently assigning defect reports to the right repairer for repair, to ensure efficiency and reduce the cost of open-source software development is an important problem that must be solved in software quality improvement. In this study, we propose a new defect report repair recommendation algorithm, RCNN, which can effectively learn the features of the defect report and recommend the appropriate repairer according to the feature. The proposed algorithm uses a CNN convolution kernel to capture the local information of the text and RNN is used to capture the sequence information of the text. The attention mechanism is introduced to learn the contribution ratio of each part of the text to the overall semantic information of the text. Thus, to a certain extent, it makes up for the defect that RNN cannot effectively learn and monitor remote information. Through experiments on the Eclipse and Mozilla datasets, compared with NB (naive Bayes), SVM (support vector machines), LeeCNN and DBRNNA, the RCNN model can effectively find the appropriate bug repairer among many repairers, and achieve higher classification accuracy.

关键词： software Maintenance software Quality Bug Report Triage Artificial Intelligence

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Towards Continuous Data Collection from In-service Products: Exploring the Relation Between Data Dimensions and Collection Challenges 28

Towards Continuous Data Collection from In-service Products:...

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28th asia-pacific software engineering conference (APSEC)

作者： Dakkak, Anas Zhang, Hongyi Mattos, David Issa Bosch, Jan Olsson, Helena Holmstrom Ericsson AB Stockholm Sweden Chalmers Univ Technol Gothenburg Sweden Malmo Univ Malmo Sweden

ISBN: (纸本)9781665437844

Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While there is no shortage of possible use cases leveraging data from in-service products, software-intensive embedded systems companies struggle to continuously collect data from their in-service products. Often, data collection is done in an ad-hoc way and targeting specific use cases or needs. Besides, few studies have investigated data collection challenges in relation to the data dimensions, which are the minimum set of quantifiable data aspects that can define software-intensive embedded product data from a collection point of view. To help address data collection challenges, and to provide companies with guidance on how to improve this process, we conducted a case study at a large multinational telecommunications supplier focusing on data characteristics and collection challenges from the Radio Access Networks (RAN) products. We further investigated the relations of these challenges to the data dimensions to increase our understanding of how data dominions contribute to the challenges.

关键词： Continuous Data Collection software-intensive embedded system in-service products

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Exploring Loss Scenarios of STPA with Reinforcement Learning: A Case Study of Platform Screen Door

Exploring Loss Scenarios of STPA with Reinforcement Learning...

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asia-pacific conference on software engineering

作者： Jiyoung Chang Ryeonggu Kwon Gihwon Kwon Dept. of Software Safety and Cyber Security University of Kyonggi Suwon Republic of Korea Dept. of Computer Science University of Kyonggi Suwon Republic of Korea Dept. of AI Computer Engineering University of Kyonggi Suwon Republic of Korea

System-Theoretic Process Analysis (STPA) is performing a potential hazard analysis in the interactions of components within a system. Among STPA's process, deriving loss scenarios by identifying causes of the hazard has become increasingly important due to the complexity of modern systems. In this study, we utilize reinforcement learning to derive state transition paths that lead to hazard as loss scenarios, and extract the frequency of risk frequencies to demonstrate the necessity of safety measures for the respective loss scenarios. It is anticipated that this efficient approach will aid in simulating the system design process and enhancing safety.

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Unit Test Based Component Integration Testing

Unit Test Based Component Integration Testing

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asia-pacific conference on software engineering

作者： Nils Wild Horst Lichter Research Group Software Construction RWTH Aachen University Aachen Germany

Effective and efficient testing of complex component based software systems is hard. Isolated test cases that test isolated components are focused and efficient but are not effective in detecting integration faults. Integration test cases on the other hand are hard to develop and maintain. In this paper we present an unit test based integration meta-model and testing approach, to test the integrateability of component-based systems based on structural and behavioral information derived from test executions of the respective components' unit test cases. The meta-model is formalized using the property graph model and integration criteria are presented to detect certain types of integration faults early on. Last but not least we propose an approach to derive integration tests from the data contained in the model.

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GHA-BFP: Framework for Automated Build Failure Prediction in GitHub Actions

GHA-BFP: Framework for Automated Build Failure Prediction in...

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asia-pacific conference on software engineering

作者： Jiatai Li Yang Zhang Tao Wang Yiwen Wu National University of Defense Technology Changsha China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

GitHub Actions (GHA), a powerful Continuous Integration and Continuous Deployment (CI/CD) service, has revolutionized the way developers automate tasks in the software development pipeline. Although GHA provides great convenience, if a GHA build fails, the time spent waiting for results and debugging is wasted, which can seriously affect development efficiency. In this study, we delve into GHA build results and introduce an automatic framework named GHA-BFP that uses ML models to predict the failure of GHA builds. Using GHA-BFP with Random Forest model, we achieved the highest performance in predicting the failure of GHA builds, with all key metrics (i.e., Accuracy, Precision, Recall, and F1 score) exceeding 75%. Furthermore, through ablation experiments, we have verified the essentiality of the four categories of input features. Lastly, we conducted an assessment of the importance of each individual input feature in relation to the model's predictive capabilities.

关键词： Radio frequency Measurement Accuracy Pipelines Debugging Predictive models Continuous integration software engineering

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Implementation of Smart Manufacturing System Learning Kit: Study of engineering Teachers' Performance and Engagement 30

Implementation of Smart Manufacturing System Learning Kit: S...

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30th International conference on Computers in Education (ICCE)

作者： Tangtrongpairos, Ornanong Howimanporn, Suppachai Pratumswan, Pornjit Raijaidee, Panupong Khanthinthara, Watcharapong Seemuang, Yuthapong Chookaew, Sasithorn Rajamangala Univ Technol Suvarnabhumi Fac Ind Educ Dept Elect & Telecommun Engn Samut Prakan Thailand King Mongkuts Univ Technol Fac Tech Educ Dept Teacher Training Mech Engn North Bangkok Thailand Siam Technol Coll Fac Engn & Technol Bangkok Thailand

ISBN: (纸本)9786269689002

Nowadays, engineering education is a shift from practice-based with textbooks to practice-based with authentic materials. Especially the learning factory paradigm is a fundamental approach that refers to a realistic manufacturing environment for education, training, and industry research. However, engineering teachers experience a lack of knowledge and technology used in smart factory learning and a lack of learning material covering the industrial manufacturing content. Due to the rapid advancements in technology, there is a need to ensure that engineering teachers receive knowledge and skills that they may not have previously. This study offers three-day professional training with smart manufacturing system learning kits that can be used to encourage teachers' learning in STEM education. The participants are engineering teachers (n=38) in electronic, mechanical, and mechatronic engineering majors. The findings show practical implementation and recommendations to enhance engineering teachers' performance and engagement, which positively attitude the learning process.

关键词： Industry 4.0 engineering education learning factory STEM education

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AlgoCert: Adopt Non-transferable NFT for the Issuance and Verification of Educational Certificates using Algorand Blockchain

AlgoCert: Adopt Non-transferable NFT for the Issuance and Ve...

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2022 IEEE asia-pacific conference on Computer Science and Data engineering, CSDE 2022

作者： Tahlil, Tahlil Gomasta, Sarmistha Sarna Ali, A B M Shawkat Institute of Information Technology University of Dhaka Dhaka Bangladesh Daffodil International University Dept of Software Engineering Dhaka Bangladesh The University of Fiji Dept of Computer Science & Engineering Fiji

ISBN: (纸本)9781665453059

In addition to tanking the economy and society, credential fraud frequently damages the credibility of higher education. As the majority of conventional verification and issuance procedures are arduous, expensive, and obsolete, fraud has proliferated in numerous forms, ranging from paper-based counterfeit certificates to replicas enabled by database manipulation, and has reached exponential quantities. In this paper, the authors propose AlgoCert, a comprehensive credential verification solution that incorporates blockchain, self-sovereign identity (SSI), and decentralized identifier (DID) to introduce a more proficient, attack-resistant system that is user-friendly as well as effective at reducing widespread credential fraud. In addition, AlgoCert leverages stateful smart contracts on the Algorand blockchain and non-transferable non-fungible tokens (NFT), and neither students nor organizations are required to handle credentials in order to use the system. This paper provides a prototype of our solution and detail our attempts to develop a verification service with features, including data security, transcript authentication, and selective data disclosure. © 2022 IEEE.

关键词： Blockchain

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NameChecker: Detecting Inconsistency between Method Names and Method Bodies 28

NameChecker: Detecting Inconsistency between Method Names an...

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28th asia-pacific software engineering conference (APSEC)

作者： Li, Kejun Wang, Taiming Liu, Hui Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665437844

Methods are basic elements for functional organization in software applications. A high-quality method name should clearly express its function, and help developers understand its usages quickly without reading through the lengthy and complex method body. However, in some cases, method names could be inconsistent with their functional implementations. The inconsistency in turn may result in inaccurate interpretation of methods, and even buggy method invocations. To this end, in this paper, we propose a deep learning-based approach, called NameChecker, to detecting the inconsistency between method names and their corresponding method bodies. NameChecker extracts lexical and structural features of source code by static code analysis. Based on the extracted features, NameChecker employs deep learning techniques (i.e., LSTM, and Attention mechanism) to predict whether the given method name is consistent with its implementation. Different from other deep learning based approaches to inconsistency detection, NameChecker avoids the generation (recommendation) of method names. Empirical studies suggested that generated method names are often incorrect, and thus avoiding method name generation may significantly improve the accuracy of NameChecker. We evaluate NameChecker on open-source applications, and our evaluation results suggest that NameChecker improves the state of the art by increasing the F1-score from 66.7% to 73.4%.

关键词： software Quality Deep Learning Static Analysis Method Name Inconsistent

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Development of College Teaching software Based on Virtual Reality Technology

Development of College Teaching Software Based on Virtual Re...

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software engineering, Social Network Analysis and Intelligent Computing (SSAIC), asia-pacific conference on

作者： Jia Zhao Guangxi Polytechnic of Construction Nanning Jiangsu China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

In order to make up for the shortcomings of engineering experimental teaching and actively respond to the call of the Ministry of Education to promote the reform of experimental teaching, and make full use of VR (Virtual Reality) technology under the background of the development of the times, this paper carries out the research on the development and application of efficient teaching software based on VR technology. Establish the VR teaching software based on 3DS MAX, and the basic platform resources are based on 3DS MAX, including all the underlying codes, prefabricated parts, models, rendering material balls, etc. that support the whole project. The configuration file can modify the parameters of menu configuration, function configuration and system configuration, so as to change the software implementation effect. The interaction mode of the system is mainly based on the physical control operation interaction and the object picking interaction. To realize the interaction with the virtual scene, the objects in the virtual scene need to make corresponding actions. The application results show that about 87% students want teachers to use VR courseware for teaching, which verifies the practicability of this teaching software.

关键词： Courseware Learning systems Solid modeling Three-dimensional displays Social networking (online) Education Virtual reality

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