Neural networks, and in particular Convolutional Neural Networks (CNNs), are often optimized using default parameters. Neural Architecture Search (NAS) enables multiple architectures to be evaluated prior to selection of the optimal architecture. A system integrating open-source tools for Neural Architecture Search (OpenNAS) of image classification problems has been developed and made available to the open-source community. OpenNAS takes any dataset of grayscale, or RGB images, and generates the optimal CNN architecture. The training and optimization of neural networks, using super learner and ensemble approaches, is explored in this research. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and pretrained models serve as base learners for network ensembles. Meta learner algorithms are subsequently applied to these base learners and the ensemble performance on image classification problems is evaluated. Our results show that a stacked generalization ensemble of heterogeneous models is the most effective approach to image classification within OpenNAS.
{"title":"Enhanced Neural Architecture Search Using Super Learner and Ensemble Approaches","authors":"Séamus Lankford, Diarmuid Grimes","doi":"10.1145/3456126.3456133","DOIUrl":"https://doi.org/10.1145/3456126.3456133","url":null,"abstract":"Neural networks, and in particular Convolutional Neural Networks (CNNs), are often optimized using default parameters. Neural Architecture Search (NAS) enables multiple architectures to be evaluated prior to selection of the optimal architecture. A system integrating open-source tools for Neural Architecture Search (OpenNAS) of image classification problems has been developed and made available to the open-source community. OpenNAS takes any dataset of grayscale, or RGB images, and generates the optimal CNN architecture. The training and optimization of neural networks, using super learner and ensemble approaches, is explored in this research. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and pretrained models serve as base learners for network ensembles. Meta learner algorithms are subsequently applied to these base learners and the ensemble performance on image classification problems is evaluated. Our results show that a stacked generalization ensemble of heterogeneous models is the most effective approach to image classification within OpenNAS.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115152748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although test case prioritization and reduction are two different problems in regression testing, they are essentially interrelated. To improve the effectiveness of regression testing, we need to perform fewer test cases, and hope to detect program faults as early as possible. However, most existing techniques haven't proposed a better solution to solve these two issues at the same time. In this paper, we present a multi-priority algorithm combine mutation testing and clustering techniques, and use clustering techniques to put test cases with similar fault-detection ability into a cluster, the multi-priority algorithm selects a high-priority test in each cluster. The results show that the multi-priority algorithm not only reduces a large number of test cases, but also obtains the results of test case prioritization better than greedy algorithm and random reduction method. Especially, the average reduction size of test cases is 40.23% in total 556018 test cases, and removing test cases that trigger real program faults only accounts for 0.7435% of all tests. Our method achieves a greater reduction in the number of test cases at the expense of a mini loss of in fault-detection ability. The effectiveness of test case prioritization is 2.63% higher than other methods. In addition, we find that the different number of clusters affect the effectiveness of test case prioritization and reduction in regression testing. 1
{"title":"An Extensive Study on Multi-Priority Algorithm in Test Case Prioritization and Reduction","authors":"Longbo Li, Yanhui Zhou, Yuan Yuan, Shenghua Wu","doi":"10.1145/3456126.3456135","DOIUrl":"https://doi.org/10.1145/3456126.3456135","url":null,"abstract":"Although test case prioritization and reduction are two different problems in regression testing, they are essentially interrelated. To improve the effectiveness of regression testing, we need to perform fewer test cases, and hope to detect program faults as early as possible. However, most existing techniques haven't proposed a better solution to solve these two issues at the same time. In this paper, we present a multi-priority algorithm combine mutation testing and clustering techniques, and use clustering techniques to put test cases with similar fault-detection ability into a cluster, the multi-priority algorithm selects a high-priority test in each cluster. The results show that the multi-priority algorithm not only reduces a large number of test cases, but also obtains the results of test case prioritization better than greedy algorithm and random reduction method. Especially, the average reduction size of test cases is 40.23% in total 556018 test cases, and removing test cases that trigger real program faults only accounts for 0.7435% of all tests. Our method achieves a greater reduction in the number of test cases at the expense of a mini loss of in fault-detection ability. The effectiveness of test case prioritization is 2.63% higher than other methods. In addition, we find that the different number of clusters affect the effectiveness of test case prioritization and reduction in regression testing. 1","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126423396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large Internet service platforms involving hundreds of inter-system calls generate a large amount of alarm data every day. How to use the network topology information and alarm data to analyze the alarm in a timely and effective manner, and finally give the effective alarm and the suspected root cause, is the main challenge facing the network operation and maintenance. This paper studies a kind of solution. First, we preprocess the output sequence of a long alarm system cluster. And then judge whether there is a root fault by Support Vector Machine. At the next stage, employee a well-prepared Bayesian network to compute the highest probability of fault types, combined with filtering rules to get the final conclusion. The method is lightweight and efficient, which has been verified by experiments.
{"title":"Network root fault location based on network topology and alarm","authors":"Jingyu Li, Yunyi Jiang, Ziye Zhang","doi":"10.1145/3456126.3456138","DOIUrl":"https://doi.org/10.1145/3456126.3456138","url":null,"abstract":"Large Internet service platforms involving hundreds of inter-system calls generate a large amount of alarm data every day. How to use the network topology information and alarm data to analyze the alarm in a timely and effective manner, and finally give the effective alarm and the suspected root cause, is the main challenge facing the network operation and maintenance. This paper studies a kind of solution. First, we preprocess the output sequence of a long alarm system cluster. And then judge whether there is a root fault by Support Vector Machine. At the next stage, employee a well-prepared Bayesian network to compute the highest probability of fault types, combined with filtering rules to get the final conclusion. The method is lightweight and efficient, which has been verified by experiments.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128733984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pooneh Mohaghegh, Rabia Saeed, F. Tièche, A. Boegli, Y. Perriard
This article demonstrates person localization using a hybrid system consisting of an electromagnetic positioning system and a depth camera to authorize access control. The ultimate aim of this system is to distinguish moving people in a defined area by tracking the RF device and the people. It focuses on the application and incorporation of the received data from these two systems. Both systems send data simultaneously which is stored in a Docker container for further analysis. The data is processed in real-time to track the movement of the targets. The centralized database monitoring grants secure access to the information. The motive for using this hybrid system lies in the ever-growing need for accurate position determination for indoor and complex environments. Track and tracing are especially important in access-control applications. The system has a great impact on real-life access-control applications in malls, shops, train stations, and generally everyplace where the access control requires monitoring. The non-blocking feature plus the accuracy can provide ease of use for the users. Moreover, employing a low-frequency tag system does not suffer from the multipath effect and non-line of sight problems that are inevitable for indoor applications. By extending the number of users for a larger area, this system can replace traditional security gates with a pleasant look and comfortable application.
{"title":"Depth Camera and Electromagnetic Field Localization System For IoT Application: High level, lightweight data fusion","authors":"Pooneh Mohaghegh, Rabia Saeed, F. Tièche, A. Boegli, Y. Perriard","doi":"10.1145/3456126.3456145","DOIUrl":"https://doi.org/10.1145/3456126.3456145","url":null,"abstract":"This article demonstrates person localization using a hybrid system consisting of an electromagnetic positioning system and a depth camera to authorize access control. The ultimate aim of this system is to distinguish moving people in a defined area by tracking the RF device and the people. It focuses on the application and incorporation of the received data from these two systems. Both systems send data simultaneously which is stored in a Docker container for further analysis. The data is processed in real-time to track the movement of the targets. The centralized database monitoring grants secure access to the information. The motive for using this hybrid system lies in the ever-growing need for accurate position determination for indoor and complex environments. Track and tracing are especially important in access-control applications. The system has a great impact on real-life access-control applications in malls, shops, train stations, and generally everyplace where the access control requires monitoring. The non-blocking feature plus the accuracy can provide ease of use for the users. Moreover, employing a low-frequency tag system does not suffer from the multipath effect and non-line of sight problems that are inevitable for indoor applications. By extending the number of users for a larger area, this system can replace traditional security gates with a pleasant look and comfortable application.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At present, the understanding of speech by machines mostly focuses on the understanding of semantics, but speech should also include emotions in the speech. Emotion can not only strengthen semantics, but can even change semantic information. The paper discusses how to realize the emotion classification, which is called SeeSpeech. SeeSpeech chooses MCEP as the speech emotion feature, and inputs it into CNN and Transformer respectively. In order to obtain richer features, CNN uses batch normalization, while Transformer uses layer normalization, and then combines the output of CNN and Transformer. Finally, the type of emotion is obtained through SoftMax. SeeSpeech obtained the highest classification accuracy rate of 97% on the RAVDESS data set, and also obtained the classification accuracy rate of 85% on the actual edge gateway test. It can be seen from the results that SeeSpeech has encouraging performance in speech emotion classification and has a wide range of application prospects in human-computer interaction.
{"title":"SeeSpeech: See Emotions in The Speech","authors":"Jianing Geng, Hao Zhu, Xiang-Yang Li","doi":"10.1145/3456126.3456129","DOIUrl":"https://doi.org/10.1145/3456126.3456129","url":null,"abstract":"At present, the understanding of speech by machines mostly focuses on the understanding of semantics, but speech should also include emotions in the speech. Emotion can not only strengthen semantics, but can even change semantic information. The paper discusses how to realize the emotion classification, which is called SeeSpeech. SeeSpeech chooses MCEP as the speech emotion feature, and inputs it into CNN and Transformer respectively. In order to obtain richer features, CNN uses batch normalization, while Transformer uses layer normalization, and then combines the output of CNN and Transformer. Finally, the type of emotion is obtained through SoftMax. SeeSpeech obtained the highest classification accuracy rate of 97% on the RAVDESS data set, and also obtained the classification accuracy rate of 85% on the actual edge gateway test. It can be seen from the results that SeeSpeech has encouraging performance in speech emotion classification and has a wide range of application prospects in human-computer interaction.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125085238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning has achieved remarkable success in a variety of tasks in real life, such as speech and vision. However, the vast computational complexity of convolution neural networks (CNN) has limited the speed of the network running in hardware. In recent years, network quantization technology has made it possible to quantize network into the 16-bit fixed point, 8-bit integer, and even binary, maintaining the original performance, while the computational complexity of the network inference is still considerable. Therefore, exploring high-performance and efficient hardware architecture designed for quantized neural networks (QNN) is necessary to eliminate the bottleneck of high-density computing requirements. FPGA is a highly parallelized hardware computing platform. The outstanding advantage is that it contains a large number of primary configurable logic resources. We explore the possibility of implementation for convolution calculations based on LUTs, introduce the integer multipliers and addition trees based on FPGAs, and propose an efficient computing architecture for QNN. With the optimization of Winograd convolution algorithm for QNN, we demonstrate that our scheme could significantly reduce the number of multipliers without using DSP resources, saving the usage of LUT resources by 2.25× at least. In the end, our LUT-based architecture for QNN will shorten the latency up to 19.3× and represent more effective performance compared other methods.
{"title":"Efficient LUT-based FPGA Accelerator Design for Universal Quantized CNN Inference","authors":"Yanpeng Cao, Changjun Song, Yongming Tang","doi":"10.1145/3456126.3456140","DOIUrl":"https://doi.org/10.1145/3456126.3456140","url":null,"abstract":"Deep learning has achieved remarkable success in a variety of tasks in real life, such as speech and vision. However, the vast computational complexity of convolution neural networks (CNN) has limited the speed of the network running in hardware. In recent years, network quantization technology has made it possible to quantize network into the 16-bit fixed point, 8-bit integer, and even binary, maintaining the original performance, while the computational complexity of the network inference is still considerable. Therefore, exploring high-performance and efficient hardware architecture designed for quantized neural networks (QNN) is necessary to eliminate the bottleneck of high-density computing requirements. FPGA is a highly parallelized hardware computing platform. The outstanding advantage is that it contains a large number of primary configurable logic resources. We explore the possibility of implementation for convolution calculations based on LUTs, introduce the integer multipliers and addition trees based on FPGAs, and propose an efficient computing architecture for QNN. With the optimization of Winograd convolution algorithm for QNN, we demonstrate that our scheme could significantly reduce the number of multipliers without using DSP resources, saving the usage of LUT resources by 2.25× at least. In the end, our LUT-based architecture for QNN will shorten the latency up to 19.3× and represent more effective performance compared other methods.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126517335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Boosting, as a meta-algorithm for ensemble learning, have been widely applied to variety popular machine learning algorithms. However, noises in training and testing datasets could significantly affect the performance of boosting algorithm. SAMME pays too much attention to samples that are not correctly classified in multiple iterations. These samples could be mislabeled samples that cannot be correctly classified, so the classifier cannot learn the actual distribution of the original data. To solve this problem, in this paper, we proposed a rectified algorithm R.SAMME based on multi-class classification algorithm SAMME by limiting the weight of each sample based on current accuracy. We evaluate our approach on UCI benchmark datasets, experiments show that R.SAMME has better performance in noisy datasets.
{"title":"Rectified Multi-class AdaBoost for Noisy Dataset Based on Weight Adjustment Standard","authors":"Keke Hu, Wanwei Liu, Tun Li","doi":"10.1145/3456126.3456143","DOIUrl":"https://doi.org/10.1145/3456126.3456143","url":null,"abstract":"Boosting, as a meta-algorithm for ensemble learning, have been widely applied to variety popular machine learning algorithms. However, noises in training and testing datasets could significantly affect the performance of boosting algorithm. SAMME pays too much attention to samples that are not correctly classified in multiple iterations. These samples could be mislabeled samples that cannot be correctly classified, so the classifier cannot learn the actual distribution of the original data. To solve this problem, in this paper, we proposed a rectified algorithm R.SAMME based on multi-class classification algorithm SAMME by limiting the weight of each sample based on current accuracy. We evaluate our approach on UCI benchmark datasets, experiments show that R.SAMME has better performance in noisy datasets.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130453945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the aging of Chinese society intensifies, aging-related community issues become increasingly serious. Visiting several hutongs and communities in Beijing, the design team conducted in-depth interviews with the elderly, with the medical staff doing the same in community hospitals and pharmacies. The service and production system design was used to classify the user population and describe the current aging phenomenon. There are still many challenges faced by the elderly community, such as self-care, nutrition, and scheduling appointments. In this paper, by examining elderly individuals' daily lives and medical challenges, our design team created user journey maps and discovered specific problems in the service process. We extracted the design touchpoints in the user experience, analyzed the user behavior, and discovered some design opportunities. We also expect that service design thinking and design morphology research methods will create innovative and optimized solutions for current aging community medical problems. This provides for on-site services such as diagnosis and treatment, nursing, and prescription and meal delivery for homebound elderly people and proposes a new model of mobile auxiliary medical system intervention in the aging community. This makes the elderly-community-product-environment more sustainable.
{"title":"Design and Research of Mobile Assisted Medical System Intervention in the Elderly Community: A Case Study","authors":"Zhiming Niu, Lele Zhang, Yu Zhang, Yu E Tian, Saehwa Choi","doi":"10.1145/3456126.3456142","DOIUrl":"https://doi.org/10.1145/3456126.3456142","url":null,"abstract":"As the aging of Chinese society intensifies, aging-related community issues become increasingly serious. Visiting several hutongs and communities in Beijing, the design team conducted in-depth interviews with the elderly, with the medical staff doing the same in community hospitals and pharmacies. The service and production system design was used to classify the user population and describe the current aging phenomenon. There are still many challenges faced by the elderly community, such as self-care, nutrition, and scheduling appointments. In this paper, by examining elderly individuals' daily lives and medical challenges, our design team created user journey maps and discovered specific problems in the service process. We extracted the design touchpoints in the user experience, analyzed the user behavior, and discovered some design opportunities. We also expect that service design thinking and design morphology research methods will create innovative and optimized solutions for current aging community medical problems. This provides for on-site services such as diagnosis and treatment, nursing, and prescription and meal delivery for homebound elderly people and proposes a new model of mobile auxiliary medical system intervention in the aging community. This makes the elderly-community-product-environment more sustainable.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130868419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The construction industry is a $6 trillion industry worldwide with a prediction to grow towards $10,3 trillion by 2023 and constitutes an essential part of the global economy. Nevertheless, the management of the construction effort is still very manual. The construction process from design, to sourcing of material, contract management, and so on, is a convoluted and intransparent process filled with risks the collaborating parties are exposed to. A need exists for management platforms that streamline and automate collaborative construction processes, establish transparency, traceability, and information symmetry between business parties. This paper presents the Construction Project Management (CoPM) platform that is based on blockchain- and smart-contract technologies for enabling peer-to-peer collaboration between construction parties that enhances the flow of information for reducing cost- and time expenditures while improving the quality of service. The CoPM system is based on diligent up-front requirement studies from which we derive a coherent system architecture and set of cooperation protocols. Thereby, the CoPM system overcomes the currently existing fractured value propositions for construction-management systems. CCS Concepts •Software and its engineering•Software creation and management•Designing software
{"title":"Designing a Collaborative Construction-Project Platform on Blockchain Technology for Transparency, Traceability, and Information Symmetry","authors":"Chibuzor Udokwu, A. Norta, Christoph Wenna","doi":"10.1145/3456126.3456134","DOIUrl":"https://doi.org/10.1145/3456126.3456134","url":null,"abstract":"The construction industry is a $6 trillion industry worldwide with a prediction to grow towards $10,3 trillion by 2023 and constitutes an essential part of the global economy. Nevertheless, the management of the construction effort is still very manual. The construction process from design, to sourcing of material, contract management, and so on, is a convoluted and intransparent process filled with risks the collaborating parties are exposed to. A need exists for management platforms that streamline and automate collaborative construction processes, establish transparency, traceability, and information symmetry between business parties. This paper presents the Construction Project Management (CoPM) platform that is based on blockchain- and smart-contract technologies for enabling peer-to-peer collaboration between construction parties that enhances the flow of information for reducing cost- and time expenditures while improving the quality of service. The CoPM system is based on diligent up-front requirement studies from which we derive a coherent system architecture and set of cooperation protocols. Thereby, the CoPM system overcomes the currently existing fractured value propositions for construction-management systems. CCS Concepts •Software and its engineering•Software creation and management•Designing software","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134070886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agnaldo O. Penha-Junior, Carlos E. De Souza, G. M. Souza, Jaqueline S. Araujo, José Ivan Bezerra Vilarouca Filho, Leon M. Barroso, Rodrigo j. B. Fernandes, Paulo Fonseca, F. Giuntini
The process of creating a global system to support the maintenance of devices is a great challenge, due to the amount of aspects involved, such as performance, security, communication, and user experience. This tends to become even more complex when the development team is multicultural and located in several countries. Therefore, this paper presents and discusses the development trajectory, as well as the challenges and overcome during the process of creating RETAS (Runtime Evaluation and Trend Analysis Service), that is an interactive platform for the execution and visualization of automated tests on smartphones. We discuss the challenges of design, implementation, and project organization of industry point of view. Besides, we show aspects of validation of the user experience and usability in the global application context. The discussions demonstrate a learning roadmap for global maintenance applications context.
创建一个支持设备维护的全局系统的过程是一个巨大的挑战，因为涉及的方面很多，例如性能、安全性、通信和用户体验。当开发团队是多元文化的，并且位于多个国家时，情况就会变得更加复杂。因此，本文介绍并讨论了RETAS (Runtime Evaluation and Trend Analysis Service，运行时评估和趋势分析服务)的发展轨迹，以及在创建过程中遇到的挑战和克服的问题。RETAS是一个用于智能手机上执行和可视化自动化测试的交互式平台。我们从行业的角度讨论了设计、实现和项目组织的挑战。此外，我们还展示了在全局应用程序上下文中验证用户体验和可用性的各个方面。讨论展示了全局维护应用程序上下文的学习路线图。
{"title":"Challenges in the Development of a Global Software User Interface by Multicultural Teams: an Industrial Experience","authors":"Agnaldo O. Penha-Junior, Carlos E. De Souza, G. M. Souza, Jaqueline S. Araujo, José Ivan Bezerra Vilarouca Filho, Leon M. Barroso, Rodrigo j. B. Fernandes, Paulo Fonseca, F. Giuntini","doi":"10.1145/3456126.3456144","DOIUrl":"https://doi.org/10.1145/3456126.3456144","url":null,"abstract":"The process of creating a global system to support the maintenance of devices is a great challenge, due to the amount of aspects involved, such as performance, security, communication, and user experience. This tends to become even more complex when the development team is multicultural and located in several countries. Therefore, this paper presents and discusses the development trajectory, as well as the challenges and overcome during the process of creating RETAS (Runtime Evaluation and Trend Analysis Service), that is an interactive platform for the execution and visualization of automated tests on smartphones. We discuss the challenges of design, implementation, and project organization of industry point of view. Besides, we show aspects of validation of the user experience and usability in the global application context. The discussions demonstrate a learning roadmap for global maintenance applications context.","PeriodicalId":431685,"journal":{"name":"2021 2nd Asia Service Sciences and Software Engineering Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131350388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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