Pub Date : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00043
Yunyun Pang, Qiang Ye, Yang Lu
This paper focuses on the problems of separation velocity deviation and satellite orbit accuracy during the satellite-rocket separation process of spring energy storage, based on the separation dynamics model of satellite-rocket separation, analyze the separation velocity deviation of satellite-rocket separation caused by the satellite centroid deviation, the upper stage centroid deviation of the rocket, the installation position offset, and use the orbit simulation software to study the influence of the orbit accuracy caused by separation velocity deviation, and summarize the influence rules and main influence factors of satellite-rocket separation velocity deviation and satellite orbit accuracy.
{"title":"Analysis of Velocity Deviation of Satellite-Rocket Separation and Orbit Accuracy of Satellite Caused by Multiple Factors","authors":"Yunyun Pang, Qiang Ye, Yang Lu","doi":"10.1109/QRS-C57518.2022.00043","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00043","url":null,"abstract":"This paper focuses on the problems of separation velocity deviation and satellite orbit accuracy during the satellite-rocket separation process of spring energy storage, based on the separation dynamics model of satellite-rocket separation, analyze the separation velocity deviation of satellite-rocket separation caused by the satellite centroid deviation, the upper stage centroid deviation of the rocket, the installation position offset, and use the orbit simulation software to study the influence of the orbit accuracy caused by separation velocity deviation, and summarize the influence rules and main influence factors of satellite-rocket separation velocity deviation and satellite orbit accuracy.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133657321","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00069
Xinwei Liu, Chuanqi Tao
Dead code is widespread in open-source and commercial software systems. While there is some work on dead code detection, there is no work on evaluating the risk of removing dead code. This paper introduces complex network into dead code evaluation and proposes a dead code evaluation method based on weighted technique for order preference by similarity to ideal solution(TOPSIS). We regard degree centrality, closeness centrality, betweeness centrality and the proportion of alive codes as the multi-attribute of weighted TOPSIS, which overcomes the shortage of using the same weight for each attribute in the original method. We evaluate dead code in two open-source Java projects. The results show that this method can well evaluate the risk of deleting dead code in nodes.
{"title":"A Dead Code Evaluation Method based on Complex Network","authors":"Xinwei Liu, Chuanqi Tao","doi":"10.1109/QRS-C57518.2022.00069","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00069","url":null,"abstract":"Dead code is widespread in open-source and commercial software systems. While there is some work on dead code detection, there is no work on evaluating the risk of removing dead code. This paper introduces complex network into dead code evaluation and proposes a dead code evaluation method based on weighted technique for order preference by similarity to ideal solution(TOPSIS). We regard degree centrality, closeness centrality, betweeness centrality and the proportion of alive codes as the multi-attribute of weighted TOPSIS, which overcomes the shortage of using the same weight for each attribute in the original method. We evaluate dead code in two open-source Java projects. The results show that this method can well evaluate the risk of deleting dead code in nodes.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115475860","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}
In the last ten years, the development of deep learning has promoted the progress of autonomous driving. Several major manufacturers, including Google, Tesla, Baidu, Audi, etc., are building and actively testing self-driving cars. However, the safety of autonomous driving still raises concerns. Recent research has used metamorphic testing to evaluate the robustness of autonomous driving models, but metamorphic relations defined during the test are basically based on equality, and there are very few inequality-based metamorphic relations. Our goal is to provide more inequality-based metamorphic relations to efficiently detect autonomous driving model violations. IEMT proposes additional inequality-based metamorphic relations and compares the robustness of autonomous driving models based on different neural network models. The experimental results show that the metamorphic relations we proposed can detect inconsistent behaviors of the driving model quite efficiently.
{"title":"IEMT: Inequality-Based Metamorphic Testing for Autonomous Driving Models","authors":"Chao Xiong, Zhiyi Zhang, Yuqian Zhou, Chen Liu, Zhiqiu Huang","doi":"10.1109/QRS-C57518.2022.00049","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00049","url":null,"abstract":"In the last ten years, the development of deep learning has promoted the progress of autonomous driving. Several major manufacturers, including Google, Tesla, Baidu, Audi, etc., are building and actively testing self-driving cars. However, the safety of autonomous driving still raises concerns. Recent research has used metamorphic testing to evaluate the robustness of autonomous driving models, but metamorphic relations defined during the test are basically based on equality, and there are very few inequality-based metamorphic relations. Our goal is to provide more inequality-based metamorphic relations to efficiently detect autonomous driving model violations. IEMT proposes additional inequality-based metamorphic relations and compares the robustness of autonomous driving models based on different neural network models. The experimental results show that the metamorphic relations we proposed can detect inconsistent behaviors of the driving model quite efficiently.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115559140","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00100
Zhongyuan Hua, Ke Ye
In this paper, an improved algorithm for differential features of multi-objective evolutionary trajectories in multi-intellectual societies is proposed. With the intervention of spatial autocorrelation theory, the data of typical objects, events and processes in social evolution are effectively linked with temporal and spatial scale constraints and the geographic raster grid as the grassroots environment. Based on the differential evolution algorithm, an algorithmic model of social evolution for assessing social complexity and community specificity is proposed.
{"title":"Colonization Strategy Algorithm: A Deviation Algorithm Optimization based on Spatial Autocorrelation Theory","authors":"Zhongyuan Hua, Ke Ye","doi":"10.1109/QRS-C57518.2022.00100","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00100","url":null,"abstract":"In this paper, an improved algorithm for differential features of multi-objective evolutionary trajectories in multi-intellectual societies is proposed. With the intervention of spatial autocorrelation theory, the data of typical objects, events and processes in social evolution are effectively linked with temporal and spatial scale constraints and the geographic raster grid as the grassroots environment. Based on the differential evolution algorithm, an algorithmic model of social evolution for assessing social complexity and community specificity is proposed.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124198965","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00088
Yahan Yu, Yun Wang, Guigang Zhang, Yi Yang, Jian Wang
Due to the existence of uncertain factors such as the power grid system itself, natural climate change and human factors, various faults will still occur in the power grid system. If the fault alarm is not responded to in time, it is likely to cause grid instability or even collapse, resulting in inestimable losses. By building a knowledge graph for massive power grid operation and maintenance information, we can achieve fast and accurate fault information reasoning and traceability, and retrieve reasonable fault resolution measures. Use artificial intelligence technology and big data to assist power grid systems to achieve more efficient operation and maintenance. Realizing the intelligent fault diagnosis of power grid is an urgent problem to be solved at present. With the rapid development and application of artificial intelligence technology, if artificial intelligence and big data technology can be applied to the fault diagnosis and analysis of power grids, this situation of relying on manual analysis will be broken, and the efficient processing of massive operation and maintenance data will be realized.
{"title":"Question Answering Algorithm for Grid Fault Diagnosis based on Graph Neural Network","authors":"Yahan Yu, Yun Wang, Guigang Zhang, Yi Yang, Jian Wang","doi":"10.1109/QRS-C57518.2022.00088","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00088","url":null,"abstract":"Due to the existence of uncertain factors such as the power grid system itself, natural climate change and human factors, various faults will still occur in the power grid system. If the fault alarm is not responded to in time, it is likely to cause grid instability or even collapse, resulting in inestimable losses. By building a knowledge graph for massive power grid operation and maintenance information, we can achieve fast and accurate fault information reasoning and traceability, and retrieve reasonable fault resolution measures. Use artificial intelligence technology and big data to assist power grid systems to achieve more efficient operation and maintenance. Realizing the intelligent fault diagnosis of power grid is an urgent problem to be solved at present. With the rapid development and application of artificial intelligence technology, if artificial intelligence and big data technology can be applied to the fault diagnosis and analysis of power grids, this situation of relying on manual analysis will be broken, and the efficient processing of massive operation and maintenance data will be realized.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124417059","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00038
Alexander Poth, Olsi Rrjolli, A. Riel
The cloud-native development with DevOps teams drives delivery speed. To keep speed in all delivery-related activities, these have to be aligned with the methods of DevOps and with the cloud-native technology and its paradigms. One important activity in the delivery chain is integration and system testing. This paper presents an option to establish cloud-native paradigms of technology driven testing activities within agile and DevOps teams in large enterprises.
{"title":"Integration- and System-Testing Aligned with Cloud-Native Approaches for DevOps","authors":"Alexander Poth, Olsi Rrjolli, A. Riel","doi":"10.1109/QRS-C57518.2022.00038","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00038","url":null,"abstract":"The cloud-native development with DevOps teams drives delivery speed. To keep speed in all delivery-related activities, these have to be aligned with the methods of DevOps and with the cloud-native technology and its paradigms. One important activity in the delivery chain is integration and system testing. This paper presents an option to establish cloud-native paradigms of technology driven testing activities within agile and DevOps teams in large enterprises.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128400372","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00083
Tianyu Xu, Guanglin Li, Jun Lu, Ziyuan Wang
The fault location method of relational tree model (BFSTRT) based on the breadth-first selection strategy is the best in locating the Minimal Failure-causing Schema (MFS). However, the BFSTRT has a defect that it cannot solve the situation of “additional test cases introduce new failure modes”, moreover, since the fault location method based on the relational tree model is to generate all sub patterns of the failure test case at one time when building the model tree, so this type of fault location method consumes large memory space. This paper proposes an Approach of Locating Minimal Failure-causing Schema for Boolean-Specification (BELF) method, which effectively solves the above two deficiencies in BFSTRT. The experimental results show that the localization efficiency of BELF is better than BFSTRT in terms of precision and recall.
{"title":"An Approach of Locating Minimal Failure-Causing Schema for Boolean-Specifications","authors":"Tianyu Xu, Guanglin Li, Jun Lu, Ziyuan Wang","doi":"10.1109/QRS-C57518.2022.00083","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00083","url":null,"abstract":"The fault location method of relational tree model (BFSTRT) based on the breadth-first selection strategy is the best in locating the Minimal Failure-causing Schema (MFS). However, the BFSTRT has a defect that it cannot solve the situation of “additional test cases introduce new failure modes”, moreover, since the fault location method based on the relational tree model is to generate all sub patterns of the failure test case at one time when building the model tree, so this type of fault location method consumes large memory space. This paper proposes an Approach of Locating Minimal Failure-causing Schema for Boolean-Specification (BELF) method, which effectively solves the above two deficiencies in BFSTRT. The experimental results show that the localization efficiency of BELF is better than BFSTRT in terms of precision and recall.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128470181","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}
Software faults constantly appear during software development and evolution. The information on various platforms for bug knowledge recording, such as Stack Overflow, is mostly stored in weak entity relational database missing linkable relationships, which results in negative impacts on knowledge reuse. To enrich the relationships between entities and construct a software fault knowledge graph, we improve the SALKU model by considering the direction of prediction results between a pair of knowledge units, and utilize it to predict the class of linkable knowledge units. Experiment results show the improved model increases the ratio of knowledge unit pairs with equivalent link prediction results from 90.2% to 100% based on the premise of ensuring precision, recall, and F1-score. Eventually, we visualize the data from Stack Overflow in the knowledge graph based on the extracted relationships.
{"title":"Visual-ISAM: A Visualization Method for Software Failure Analysis and Evaluation based on Knowledge Graph Utilizing Improved SALKU Model","authors":"Canwei Shi, Ling-lin Gong, Qi Shao, Qi Yao, Zhiyu Duan","doi":"10.1109/QRS-C57518.2022.00047","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00047","url":null,"abstract":"Software faults constantly appear during software development and evolution. The information on various platforms for bug knowledge recording, such as Stack Overflow, is mostly stored in weak entity relational database missing linkable relationships, which results in negative impacts on knowledge reuse. To enrich the relationships between entities and construct a software fault knowledge graph, we improve the SALKU model by considering the direction of prediction results between a pair of knowledge units, and utilize it to predict the class of linkable knowledge units. Experiment results show the improved model increases the ratio of knowledge unit pairs with equivalent link prediction results from 90.2% to 100% based on the premise of ensuring precision, recall, and F1-score. Eventually, we visualize the data from Stack Overflow in the knowledge graph based on the extracted relationships.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128503230","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00123
Xuedong Ou, J. Liu
Log analysis is quite significant for reliability issues in large cloud data centers. There are noticeable problems in log anomaly detection, such as single feature extraction, unsatisfactory anomaly detection effect. In this paper, we propose a novel log anomaly detection method, which could be divided into two related parts. First, a dataset partitioning method is proposed, named K-fold Sub Hold-out Method (KSHM), which is built on the features of logs to preserve the temporality of training data when sampling. KSHM could enhance the effectiveness of sampling without increasing the number of samples, and change the way the model is trained. Second, an anomaly detection model based on hybrid Transformer-BiLSTM (TFBL) is well constructed, which could extract both temporal and semantic features of logs to serve as a source of features for comprehensive anomaly detection. Experiment results show that TFBL outperforms baseline methods in assessment criteria of accuracy, precision and F1-score, and our log anomaly detection method based on integrated KSHM and TFBL also has better anomaly detection performence.
日志分析对于大型云数据中心的可靠性问题非常重要。在日志异常检测中存在特征提取单一、异常检测效果不理想等问题。本文提出了一种新的测井异常检测方法，该方法可分为两个相关部分。首先，提出了一种基于日志特征的数据集划分方法K-fold Sub - hold method (KSHM)，该方法在采样时保持训练数据的时效性;KSHM可以在不增加样本数量的情况下提高采样的有效性，并改变模型的训练方式。其次，构建了基于混合Transformer-BiLSTM (TFBL)的异常检测模型，该模型可以同时提取日志的时间特征和语义特征，作为综合异常检测的特征源;实验结果表明，TFBL在准确度、精密度和f1评分的评价指标上优于基线方法，基于KSHM和TFBL的测井异常检测方法也具有更好的异常检测性能。
{"title":"Log Anomaly Detection Method based on Hybrid Transformer-BiLSTM Models","authors":"Xuedong Ou, J. Liu","doi":"10.1109/QRS-C57518.2022.00123","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00123","url":null,"abstract":"Log analysis is quite significant for reliability issues in large cloud data centers. There are noticeable problems in log anomaly detection, such as single feature extraction, unsatisfactory anomaly detection effect. In this paper, we propose a novel log anomaly detection method, which could be divided into two related parts. First, a dataset partitioning method is proposed, named K-fold Sub Hold-out Method (KSHM), which is built on the features of logs to preserve the temporality of training data when sampling. KSHM could enhance the effectiveness of sampling without increasing the number of samples, and change the way the model is trained. Second, an anomaly detection model based on hybrid Transformer-BiLSTM (TFBL) is well constructed, which could extract both temporal and semantic features of logs to serve as a source of features for comprehensive anomaly detection. Experiment results show that TFBL outperforms baseline methods in assessment criteria of accuracy, precision and F1-score, and our log anomaly detection method based on integrated KSHM and TFBL also has better anomaly detection performence.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128538755","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 : 2022-12-01DOI: 10.1109/QRS-C57518.2022.00060
Jiandong Li, Shaoying Liu
Fault prevention is important for software quality assurance. In this paper, we propose an approach for software fault prevention. The fault prevention effect is achieved by means of inferring correct order of implementing components in the formal specification, and automatic code generation for various components or fragments of components in a SOFL specification. The expected effect of the proposed approach is to provide guidance to programmers in formal specification-based programming, enhance their productivity and help them reduce the risk of introducing faults into software.
{"title":"Requirements-Related Fault Prevention Mechanism for SOFL Formal Specification-Based Programming","authors":"Jiandong Li, Shaoying Liu","doi":"10.1109/QRS-C57518.2022.00060","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00060","url":null,"abstract":"Fault prevention is important for software quality assurance. In this paper, we propose an approach for software fault prevention. The fault prevention effect is achieved by means of inferring correct order of implementing components in the formal specification, and automatic code generation for various components or fragments of components in a SOFL specification. The expected effect of the proposed approach is to provide guidance to programmers in formal specification-based programming, enhance their productivity and help them reduce the risk of introducing faults into software.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182267","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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