Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00038
Takuto Ohka, Shun Matsumoto, Masatsugu Ichino, H. Yoshiura
Identifying people from anonymous location histories is important for two purposes. i.e. to clarify privacy risks in using the location histories and to find evidence of who went where and when. Although linking with social network accounts is an excellent approach for such identification, previous methods need information about social relationships and have a limitation on the number of target data sets. Moreover, they make limited use of time information. We present models that overcome these problems by estimating the sameness and difference of people by using combinations of time and distance. Our proposed method uses these models along with multi-resolution models for both sides of linking, i.e. location histories and social network accounts. Evaluation using real data demonstrated the effectiveness of our method even when linking only one pseudonymized and obfuscated location history to 1 of 10,000 social network accounts without any information about social relationships.
{"title":"Time-aware multi-resolutional approach to re-identifying location histories by using social networks","authors":"Takuto Ohka, Shun Matsumoto, Masatsugu Ichino, H. Yoshiura","doi":"10.1109/QRS-C51114.2020.00038","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00038","url":null,"abstract":"Identifying people from anonymous location histories is important for two purposes. i.e. to clarify privacy risks in using the location histories and to find evidence of who went where and when. Although linking with social network accounts is an excellent approach for such identification, previous methods need information about social relationships and have a limitation on the number of target data sets. Moreover, they make limited use of time information. We present models that overcome these problems by estimating the sameness and difference of people by using combinations of time and distance. Our proposed method uses these models along with multi-resolution models for both sides of linking, i.e. location histories and social network accounts. Evaluation using real data demonstrated the effectiveness of our method even when linking only one pseudonymized and obfuscated location history to 1 of 10,000 social network accounts without any information about social relationships.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115902094","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00029
Xudong He
Blockchain technology has gained wide acceptance in recent years. Smart contracts facilitate the application of the blockchain technology. Smart contracts are programs running distributed environments and are thus error prone. Smart contracts often lack precise specifications and are written in high-level programming languages such as Solidity. In this paper, we present an approach to formally model and analyze smart contracts using predicate transitions nets. We use the blind auction smart contract to demonstrate our approach, which reveals some problematic implementation of some smart contract functions. We have applied predicate transition nets in modeling and analyzing all 11 smart contracts in Azure blockchain workbench. Although we cannot tell whether there is any problem in these smart contracts based on their informal descriptions and Solidity programs without designer input. Our experience has shown the applicability and suitability of predicate transition nets. We believe that our approach can help smart contract designers to detect and prevent early design problems in the current practice of using informal textual descriptions of smart contracts.
{"title":"Modeling and Analyzing Smart Contracts using Predicate Transition Nets","authors":"Xudong He","doi":"10.1109/QRS-C51114.2020.00029","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00029","url":null,"abstract":"Blockchain technology has gained wide acceptance in recent years. Smart contracts facilitate the application of the blockchain technology. Smart contracts are programs running distributed environments and are thus error prone. Smart contracts often lack precise specifications and are written in high-level programming languages such as Solidity. In this paper, we present an approach to formally model and analyze smart contracts using predicate transitions nets. We use the blind auction smart contract to demonstrate our approach, which reveals some problematic implementation of some smart contract functions. We have applied predicate transition nets in modeling and analyzing all 11 smart contracts in Azure blockchain workbench. Although we cannot tell whether there is any problem in these smart contracts based on their informal descriptions and Solidity programs without designer input. Our experience has shown the applicability and suitability of predicate transition nets. We believe that our approach can help smart contract designers to detect and prevent early design problems in the current practice of using informal textual descriptions of smart contracts.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"25 21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125764058","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00123
Peng Sun, Liang Luo, Shangxin Liu, Weifeng Wu
Mobile Edge Computing received significant attention in recent years. MEC can effectively reduce the data transmission pressure from end to cloud, while meeting the requirements of low latency and high bandwidth in 5G scenarios, and has wide application prospects in industrial and medical fields. In this paper, we propose to adopt the deployment of computing resources in the telecom operator's C-RAN (Centralized Radio Access Network) to form a landing solution for MEC. At the same time, it is combined with smart street light equipped with 5G base stations to form the IoT front-end of the C-RAN network for data collection. Finally, an adaptive rule engine is used to routinely monitor data and detect data anomalies in a timely manner. The anomaly monitoring solution can meet the rapid response capability to anomalies in 5G communication.
{"title":"Adaptive Rule Engine for Anomaly Detection in 5G Mobile Edge Computing","authors":"Peng Sun, Liang Luo, Shangxin Liu, Weifeng Wu","doi":"10.1109/QRS-C51114.2020.00123","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00123","url":null,"abstract":"Mobile Edge Computing received significant attention in recent years. MEC can effectively reduce the data transmission pressure from end to cloud, while meeting the requirements of low latency and high bandwidth in 5G scenarios, and has wide application prospects in industrial and medical fields. In this paper, we propose to adopt the deployment of computing resources in the telecom operator's C-RAN (Centralized Radio Access Network) to form a landing solution for MEC. At the same time, it is combined with smart street light equipped with 5G base stations to form the IoT front-end of the C-RAN network for data collection. Finally, an adaptive rule engine is used to routinely monitor data and detect data anomalies in a timely manner. The anomaly monitoring solution can meet the rapid response capability to anomalies in 5G communication.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133732915","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00112
Jie Jian, Xiaotong Zhang, Ping-Ping Ma
This paper attempts to understand the theoretical connotation of creative computing constructively through an experimental study. In this experiment, the somatosensory game designed and developed by integrating multidisciplinary knowledge and human power can help leaners develop their intention understanding ability. The data showed that the leaners in the experimental group performed better in intention understanding tasks in the somatosensory interaction environment. The activation of motor related brain regions in mirror neurons may be the basis for understanding the experimental results. From the design and conclusion of this experiment, we constructively verify that a creative computing based method is an effective one.
{"title":"Creative Computing based Experimental Study of Somatosensory Games for Promoting Intention Understanding","authors":"Jie Jian, Xiaotong Zhang, Ping-Ping Ma","doi":"10.1109/QRS-C51114.2020.00112","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00112","url":null,"abstract":"This paper attempts to understand the theoretical connotation of creative computing constructively through an experimental study. In this experiment, the somatosensory game designed and developed by integrating multidisciplinary knowledge and human power can help leaners develop their intention understanding ability. The data showed that the leaners in the experimental group performed better in intention understanding tasks in the somatosensory interaction environment. The activation of motor related brain regions in mirror neurons may be the basis for understanding the experimental results. From the design and conclusion of this experiment, we constructively verify that a creative computing based method is an effective one.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131442560","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00072
Godswill Lucky, F. Jjunju, A. Marshall
The development of an accurate, efficient and lightweight distributed solution for the detection and prevention of DDoS attacks provides network designers with new options to monitor and secure networks according to their strategic needs. Here we present, a lightweight architecture that distinguishes attack network flows from normal traffic flows with a detection accuracy of over 99.9%. The architecture presented is optimised for deployment in low-cost environments for efficient, rapid detection and prevention of DDoS attacks. To achieve a computationally efficiency architecture, the system was trained with a minimal number of features using a robust features selection approach and validated against the CIC 2017 and 2019 datasets. Analysis of the design is presented and results shows that the new architecture uses just 7% processing power of the detection system and provides no additional overhead to the monitored network.
{"title":"A Lightweight Decision-Tree Algorithm for detecting DDoS flooding attacks","authors":"Godswill Lucky, F. Jjunju, A. Marshall","doi":"10.1109/QRS-C51114.2020.00072","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00072","url":null,"abstract":"The development of an accurate, efficient and lightweight distributed solution for the detection and prevention of DDoS attacks provides network designers with new options to monitor and secure networks according to their strategic needs. Here we present, a lightweight architecture that distinguishes attack network flows from normal traffic flows with a detection accuracy of over 99.9%. The architecture presented is optimised for deployment in low-cost environments for efficient, rapid detection and prevention of DDoS attacks. To achieve a computationally efficiency architecture, the system was trained with a minimal number of features using a robust features selection approach and validated against the CIC 2017 and 2019 datasets. Analysis of the design is presented and results shows that the new architecture uses just 7% processing power of the detection system and provides no additional overhead to the monitored network.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132818320","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00122
Sa Meng, Liang Luo, Peng Sun, Yuan Gao
The public cloud is a type of cloud computing offered by third-party providers over the public Internet, making them available to Internet users. The public cloud is featured in large-scale, high complexity, dynamic resource change. However, how to provide secure and reliable cloud services to the widest range of Internet users is a big challenge for the development of cloud computing. Blockchain is a new decentralized distributed computing paradigm. The data stored in the blockchain has the characteristics of unforgeability, whole process trace, traceability, openness and transparency, and collective maintenance. Based on these characteristics, blockchain has laid a solid foundation of trust and created a reliable cooperation mechanism. Applying blockchain technology to the cloud computing platform and improving the service quality of the cloud computing platform by using the blockchain mechanism is a research topic with great application prospects.
{"title":"Reliability Service Assurance in Public Clouds based on Blockchain","authors":"Sa Meng, Liang Luo, Peng Sun, Yuan Gao","doi":"10.1109/QRS-C51114.2020.00122","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00122","url":null,"abstract":"The public cloud is a type of cloud computing offered by third-party providers over the public Internet, making them available to Internet users. The public cloud is featured in large-scale, high complexity, dynamic resource change. However, how to provide secure and reliable cloud services to the widest range of Internet users is a big challenge for the development of cloud computing. Blockchain is a new decentralized distributed computing paradigm. The data stored in the blockchain has the characteristics of unforgeability, whole process trace, traceability, openness and transparency, and collective maintenance. Based on these characteristics, blockchain has laid a solid foundation of trust and created a reliable cooperation mechanism. Applying blockchain technology to the cloud computing platform and improving the service quality of the cloud computing platform by using the blockchain mechanism is a research topic with great application prospects.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134081074","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00051
Changbo Hou, Xuejiao Zhao, Yun Lin
It is still challenging to efficiently construct semantic map with a monocular camera. In this paper, deep learning is introduced to combined with SLAM to realize semantic map production. We replace depth estimation module of SLAM with FCN which effectively solves the contradiction of triangulation. The Fc layers of FCN are modified to convolutional layers. Redundant calculation of Fc layers is avoided after optimization, and images can be input in any size. Besides, Faster RCNN, namely, a two-stage object detection network is utilized to obtain semantic information. We fine-tune RPN and Fc layers by transfer learning. The two algorithms are evaluated on official dataset. Results show that the average relative error of depth estimation is reduced by 12.6%, the accuracy of object detection is improved by 10.9%. The feasibility of the combination of deep learning and SLAM is verified.
{"title":"Depth Estimation and Object Detection for Monocular Semantic SLAM Using Deep Convolutional Network","authors":"Changbo Hou, Xuejiao Zhao, Yun Lin","doi":"10.1109/QRS-C51114.2020.00051","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00051","url":null,"abstract":"It is still challenging to efficiently construct semantic map with a monocular camera. In this paper, deep learning is introduced to combined with SLAM to realize semantic map production. We replace depth estimation module of SLAM with FCN which effectively solves the contradiction of triangulation. The Fc layers of FCN are modified to convolutional layers. Redundant calculation of Fc layers is avoided after optimization, and images can be input in any size. Besides, Faster RCNN, namely, a two-stage object detection network is utilized to obtain semantic information. We fine-tune RPN and Fc layers by transfer learning. The two algorithms are evaluated on official dataset. Results show that the average relative error of depth estimation is reduced by 12.6%, the accuracy of object detection is improved by 10.9%. The feasibility of the combination of deep learning and SLAM is verified.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134213977","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00057
Guozhen Gao, Z. Que, Zhengguo Xu
Recently deep learning based remaining useful life (RUL) prediction approaches have gained increasing attention due to their scalability and generalization ability. Although deep learning based approaches can obtain promising point prediction performance, it is not easy for them to estimate the uncertainty in RUL prediction. In this paper, a recurrent neural process model is proposed to address the prognostics uncertainty problem based on deep learning. Compared with the original neural process model, a recurrent layer is added to extract sequential information from input sliding windows. The RUL prediction problem can be considered as finding a regression function mapping the sliding window input to its corresponding RUL. By obtaining the distribution over the regression functions, the recurrent neural process is able to model the probability distribution of the RUL. As a probabilistic model, stochastic variational inference and reparameterization trick is applied to learn the parameters of the model. The proposed method is validated through the C-MAPSS turbofan engine dataset.
{"title":"Predicting Remaining Useful Life with Uncertainty Using Recurrent Neural Process","authors":"Guozhen Gao, Z. Que, Zhengguo Xu","doi":"10.1109/QRS-C51114.2020.00057","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00057","url":null,"abstract":"Recently deep learning based remaining useful life (RUL) prediction approaches have gained increasing attention due to their scalability and generalization ability. Although deep learning based approaches can obtain promising point prediction performance, it is not easy for them to estimate the uncertainty in RUL prediction. In this paper, a recurrent neural process model is proposed to address the prognostics uncertainty problem based on deep learning. Compared with the original neural process model, a recurrent layer is added to extract sequential information from input sliding windows. The RUL prediction problem can be considered as finding a regression function mapping the sliding window input to its corresponding RUL. By obtaining the distribution over the regression functions, the recurrent neural process is able to model the probability distribution of the RUL. As a probabilistic model, stochastic variational inference and reparameterization trick is applied to learn the parameters of the model. The proposed method is validated through the C-MAPSS turbofan engine dataset.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134252709","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}
Pub Date : 2020-12-01DOI: 10.1109/qrs-c51114.2020.00114
Halit Alptekin, Simge Demir, Şevval Şimşek, Cemal Yilmaz
Vulnerability assessment is the process of identifying and prioritizing the vulnerabilities in a system. Vulnerability scanners can, for example, scan a website for known vulnerabilities by running a repository of security tests, each of which is designed to reveal a known vulnerability. As the security tests need to be executed on each and every web page encountered, it may take quite a while for these scanners to report vulnerabilities. In this work, we present an approach for revealing the vulnerabilities faster by prioritizing the executions of the security tests on a per web page basis. The approach is based on a simple conjecture that “similar” web pages may possess “similar” vulnerabilities and that identifying these similarities can help prioritize the security tests. The results of the experiments we carried out by using 2927 distinct web pages (collected from 80 web sites), support our basic hypothesis; the percentages of the times the actual vulnerabilities appear in the top 8 and 15 predicted vulnerabilities were 86.9% and 98.4%, respectively.
{"title":"Towards Prioritizing Vulnerability Testing","authors":"Halit Alptekin, Simge Demir, Şevval Şimşek, Cemal Yilmaz","doi":"10.1109/qrs-c51114.2020.00114","DOIUrl":"https://doi.org/10.1109/qrs-c51114.2020.00114","url":null,"abstract":"Vulnerability assessment is the process of identifying and prioritizing the vulnerabilities in a system. Vulnerability scanners can, for example, scan a website for known vulnerabilities by running a repository of security tests, each of which is designed to reveal a known vulnerability. As the security tests need to be executed on each and every web page encountered, it may take quite a while for these scanners to report vulnerabilities. In this work, we present an approach for revealing the vulnerabilities faster by prioritizing the executions of the security tests on a per web page basis. The approach is based on a simple conjecture that “similar” web pages may possess “similar” vulnerabilities and that identifying these similarities can help prioritize the security tests. The results of the experiments we carried out by using 2927 distinct web pages (collected from 80 web sites), support our basic hypothesis; the percentages of the times the actual vulnerabilities appear in the top 8 and 15 predicted vulnerabilities were 86.9% and 98.4%, respectively.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115799301","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}
Pub Date : 2020-12-01DOI: 10.1109/QRS-C51114.2020.00060
H. Tao, Yixiang Chen, Hengyang Wu
In order to make the software trustworthiness measures more rigorous, we once applied axiomatic approaches to measure software trustworthiness, and established a software trustworthiness measure based on the decomposition of attributes (STMBDA for short). For the sake of validating the effectiveness of STMBDA, in this paper we use it to assess the trustworthiness of 23 spacecraft softwares whose total code is about 300,000 lines. The validation result shows that STMBDA can effectively evaluate the trustworthiness of the spacecraft softwares and identify the weak links in the development process.
{"title":"Decomposition of Attributes Oriented Software Trustworthiness Measure Based on Axiomatic Approaches","authors":"H. Tao, Yixiang Chen, Hengyang Wu","doi":"10.1109/QRS-C51114.2020.00060","DOIUrl":"https://doi.org/10.1109/QRS-C51114.2020.00060","url":null,"abstract":"In order to make the software trustworthiness measures more rigorous, we once applied axiomatic approaches to measure software trustworthiness, and established a software trustworthiness measure based on the decomposition of attributes (STMBDA for short). For the sake of validating the effectiveness of STMBDA, in this paper we use it to assess the trustworthiness of 23 spacecraft softwares whose total code is about 300,000 lines. The validation result shows that STMBDA can effectively evaluate the trustworthiness of the spacecraft softwares and identify the weak links in the development process.","PeriodicalId":358174,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C)","volume":"17 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114108950","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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