CPS have undergone significant evolution, integrating computational elements with physical processes to create intelligent, interconnected systems. However, their interconnected nature exposes them to diverse security threats. Authentication emerges as a critical aspect of CPS security, yet traditional methods face limitations, particularly for resource-constrained devices. In this article, we address these challenges by proposing a secure and efficient authentication mechanism based on elliptic curve cryptography. We provide a formal proof of its security using the Mao–Boyd logic and Proverif, ensuring resilience against known attacks. In addition, we compare the performance of the proposed scheme with other established schemes, demonstrating its effectiveness in enhancing CPS security without compromising operational efficiency. Proposed research contributes to the ongoing effort to fortify CPS against evolving cyber threats, fostering safer and more resilient systems for various applications.
{"title":"ECC-Based Lightweight Authentication for Resource-Constrained Devices Leveraging the Edge Node","authors":"Mitrup Kabi;Neelam Dayal;Pushpa Raikwal","doi":"10.1109/TR.2025.3562300","DOIUrl":"https://doi.org/10.1109/TR.2025.3562300","url":null,"abstract":"CPS have undergone significant evolution, integrating computational elements with physical processes to create intelligent, interconnected systems. However, their interconnected nature exposes them to diverse security threats. Authentication emerges as a critical aspect of CPS security, yet traditional methods face limitations, particularly for resource-constrained devices. In this article, we address these challenges by proposing a secure and efficient authentication mechanism based on elliptic curve cryptography. We provide a formal proof of its security using the Mao–Boyd logic and Proverif, ensuring resilience against known attacks. In addition, we compare the performance of the proposed scheme with other established schemes, demonstrating its effectiveness in enhancing CPS security without compromising operational efficiency. Proposed research contributes to the ongoing effort to fortify CPS against evolving cyber threats, fostering safer and more resilient systems for various applications.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 2","pages":"2605-2612"},"PeriodicalIF":5.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article presents research on the reliability of the switching-on process of electromagnetically actuated alternating current (AC) low voltage relays powered by distorted voltage waveforms. The aim of the research was to assess the influence of the nature and level of supply source distortions on particular switching-on time-related parameters. A measurement stand and software developed for the purposes of the experiments allowed for recognizing the impact of particular factors describing dynamics of the switching-on process. During tests, driving coils of relays were powered through the various AC voltage waveforms characterized by similar total harmonic distortion (THD) values that were applied at different phases of voltage waveform. Performed laboratory measurements showed the various impact of individual parameters (RMS value, shape, harmonic content, THD factor) on the reliability of the switching-on operation, e.g., switching time, contact loss, connection reliability. The carried-out analysis indicated the main factors and their impact level upon the reliability of the switching-on process.
{"title":"Study of the Reliability of the Switching-On Process of LV Relays Powered by Distorted AC Supply Source","authors":"Dariusz Smugala;Pawel Albrechtowicz","doi":"10.1109/TR.2025.3540517","DOIUrl":"https://doi.org/10.1109/TR.2025.3540517","url":null,"abstract":"The article presents research on the reliability of the switching-<sc>on</small> process of electromagnetically actuated alternating current (AC) low voltage relays powered by distorted voltage waveforms. The aim of the research was to assess the influence of the nature and level of supply source distortions on particular switching-<sc>on</small> time-related parameters. A measurement stand and software developed for the purposes of the experiments allowed for recognizing the impact of particular factors describing dynamics of the switching-<sc>on</small> process. During tests, driving coils of relays were powered through the various AC voltage waveforms characterized by similar total harmonic distortion (THD) values that were applied at different phases of voltage waveform. Performed laboratory measurements showed the various impact of individual parameters (RMS value, shape, harmonic content, THD factor) on the reliability of the switching-<sc>on</small> operation, e.g., switching time, contact loss, connection reliability. The carried-out analysis indicated the main factors and their impact level upon the reliability of the switching-<sc>on</small> process.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4185-4193"},"PeriodicalIF":5.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Support matrix machine (SMM) based methods have revolutionized the field of state identification by effectively mining correlations between fault features. However, some flaws limit its ability to handle interfered and limited samples, deriving from the purely focus on the closer samples nearing classify boundary and the thin design of binary classification nature, thus resulting SMM ignores the correlations between different samples and cannot align with the reality on the limited multiclass fault data. To address this issue, a novel approach called multiclass graph embedding support matrix machine (MGESMM) is proposed in this article. First, similarity matrix composed of similarity coefficient between each two samples are calculated by cosine distance. This similarity matrix is then used in manifold regularization-based graph embedding model, which can eliminate the negative impact of interfered and limited samples. Second, hamming loss-based predict error evaluation and multiclass loss-based boundary constraint is designed to form a direct multiclass classification constraint, thus the drawbacks of one-versus-one or one-versus-rest strategies for multiclass classification are prevented. Finally, to evaluate the efficacy of MGESMM, two roller bearing damage identification experiments are analyzed, and the results demonstrate that MGESMM achieves superior performance under different operating conditions.
{"title":"A Multiclass Graph Embedding Matrix Classification Method for Roller Bearing State Identification Under Limited Sample","authors":"Haiyang Pan;Haifeng Xu;Jian Cheng;Jinde Zheng;Jinyu Tong","doi":"10.1109/TR.2025.3530441","DOIUrl":"https://doi.org/10.1109/TR.2025.3530441","url":null,"abstract":"Support matrix machine (SMM) based methods have revolutionized the field of state identification by effectively mining correlations between fault features. However, some flaws limit its ability to handle interfered and limited samples, deriving from the purely focus on the closer samples nearing classify boundary and the thin design of binary classification nature, thus resulting SMM ignores the correlations between different samples and cannot align with the reality on the limited multiclass fault data. To address this issue, a novel approach called multiclass graph embedding support matrix machine (MGESMM) is proposed in this article. First, similarity matrix composed of similarity coefficient between each two samples are calculated by cosine distance. This similarity matrix is then used in manifold regularization-based graph embedding model, which can eliminate the negative impact of interfered and limited samples. Second, hamming loss-based predict error evaluation and multiclass loss-based boundary constraint is designed to form a direct multiclass classification constraint, thus the drawbacks of one-versus-one or one-versus-rest strategies for multiclass classification are prevented. Finally, to evaluate the efficacy of MGESMM, two roller bearing damage identification experiments are analyzed, and the results demonstrate that MGESMM achieves superior performance under different operating conditions.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3824-3832"},"PeriodicalIF":5.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliability assessment of multiprocessor systems presents the theoretical foundation for the layout and optimization of multiprocessor systems. The $h$-extra edge-connectivity $lambda _{h}$ and the $k$-component edge-connectivity $clambda _{k}$, as extensions of the classical edge connectivity, are two precise metrics for the measurement of the reliability of multiprocessor systems. For multiprocessor systems, determining $clambda _{k}$ and $lambda _{k}$ of a large $k$ is still difficult. Let $delta _{G}(0)=0$ and $delta _{G}(i)=frac{1}{2}(text{ex}_{i+1}(G)-text{ex}_{i}(G))$ for $iin lbrace 1, ldots, |G|-1rbrace$, where $text{ex}_{i}(G)={mathrm{max}}lbrace 2|E(G[S])|: Ssubseteq V(G), |S|=i rbrace$. In this article, we obtain $clambda _{k}$ and $lambda _{h}$ of the Cartesian powers of the $d$-regular graphs for which the lexicographic order yields an optimal order and $delta _{G}(i)leq frac{d}{2}$ for $i=0, 1, ldots, lfloor frac{|G|-1}{2}rfloor$. Our result improves some previous results about $clambda _{k}$ of Hamming graphs by Yang et al. (2023), and $lambda _{h}$ of the Cartesian powers of the complete graph $K_{4}$ by Tian et al. (2022).
{"title":"A Note on the Component (Extra) Edge Connectivity of the Cartesian Powers of Regular Multiprocessor Systems","authors":"Liqiong Xu;Shuming Zhou","doi":"10.1109/TR.2025.3540068","DOIUrl":"https://doi.org/10.1109/TR.2025.3540068","url":null,"abstract":"Reliability assessment of multiprocessor systems presents the theoretical foundation for the layout and optimization of multiprocessor systems. The <inline-formula><tex-math>$h$</tex-math></inline-formula>-extra edge-connectivity <inline-formula><tex-math>$lambda _{h}$</tex-math></inline-formula> and the <inline-formula><tex-math>$k$</tex-math></inline-formula>-component edge-connectivity <inline-formula><tex-math>$clambda _{k}$</tex-math></inline-formula>, as extensions of the classical edge connectivity, are two precise metrics for the measurement of the reliability of multiprocessor systems. For multiprocessor systems, determining <inline-formula><tex-math>$clambda _{k}$</tex-math></inline-formula> and <inline-formula><tex-math>$lambda _{k}$</tex-math></inline-formula> of a large <inline-formula><tex-math>$k$</tex-math></inline-formula> is still difficult. Let <inline-formula><tex-math>$delta _{G}(0)=0$</tex-math></inline-formula> and <inline-formula><tex-math>$delta _{G}(i)=frac{1}{2}(text{ex}_{i+1}(G)-text{ex}_{i}(G))$</tex-math></inline-formula> for <inline-formula><tex-math>$iin lbrace 1, ldots, |G|-1rbrace$</tex-math></inline-formula>, where <inline-formula><tex-math>$text{ex}_{i}(G)={mathrm{max}}lbrace 2|E(G[S])|: Ssubseteq V(G), |S|=i rbrace$</tex-math></inline-formula>. In this article, we obtain <inline-formula><tex-math>$clambda _{k}$</tex-math></inline-formula> and <inline-formula><tex-math>$lambda _{h}$</tex-math></inline-formula> of the Cartesian powers of the <inline-formula><tex-math>$d$</tex-math></inline-formula>-regular graphs for which the lexicographic order yields an optimal order and <inline-formula><tex-math>$delta _{G}(i)leq frac{d}{2}$</tex-math></inline-formula> for <inline-formula><tex-math>$i=0, 1, ldots, lfloor frac{|G|-1}{2}rfloor$</tex-math></inline-formula>. Our result improves some previous results about <inline-formula><tex-math>$clambda _{k}$</tex-math></inline-formula> of Hamming graphs by Yang et al. (2023), and <inline-formula><tex-math>$lambda _{h}$</tex-math></inline-formula> of the Cartesian powers of the complete graph <inline-formula><tex-math>$K_{4}$</tex-math></inline-formula> by Tian et al. (2022).","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4245-4252"},"PeriodicalIF":5.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify; for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.
{"title":"Field-Based Security Testing of SDN Configuration Updates","authors":"Jahanzaib Malik;Fabrizio Pastore","doi":"10.1109/TR.2025.3531654","DOIUrl":"https://doi.org/10.1109/TR.2025.3531654","url":null,"abstract":"Software-defined systems revolutionized the management of hardware devices but introduced quality assurance challenges that remain to be tackled. For example, software defined networks (SDNs) became a key technology for the prompt reconfigurations of network services in many sectors including telecommunications, data centers, financial services, cloud providers, and manufacturing industry. Unfortunately, reconfigurations may lead to mistakes that compromise the dependability of the provided services. In this article, we focus on the reconfigurations of network services in the satellite communication sector, and target security requirements, which are often hard to verify; for example, although connectivity may function properly, confidentiality may be broken by packets forwarded to a wrong destination. We propose an approach for FIeld-based Security Testing of SDN Configurations Updates (FISTS). First, it probes the network before and after configuration updates. Then, using the collected data, it relies on unsupervised machine learning algorithms to prioritize the inspection of suspicious node responses, after identifying the network nodes that likely match across the two configurations. Our empirical evaluation has been conducted with network data from simulated and real SDN configuration updates for our industry partner, a world-leading satellite operator. Our results show that, when combined with K-Nearest Neighbor, FISTS leads to best results (up to 0.95 precision and 1.00 recall). Further, we demonstrated its scalability.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3469-3483"},"PeriodicalIF":5.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Test case prioritization (TCP) has been widely used in software testing, which aims to execute test cases that are more likely to detect faults earlier than others. Among many proposed TCP approaches, lexicographical ordering-based TCP (LO-TCP) can effectively resolve ties encountered in the prioritization process, leading to better performance than original TCP approaches. However, the current LO-TCP needs to use the white-box information such as the code coverage of the program under test, which may be infeasible in some black-box testing applications such as software product lines (SPLs). In this article, we transfer the traditional LO-TCP to SPL testing by leveraging test configuration coverage instead of code coverage, and also empirically conduct some simulations and evaluate the large-scale real-world programs with real faults. The experimental results show that LO-TCP can have better performance for testing SPLs, as compared with traditional TCP approaches.
{"title":"Applying Lexicographical Ordering to Software Product Line Testing","authors":"Tao Li;Chenhui Cui;Yinyin Xu;Rubing Huang","doi":"10.1109/TR.2025.3540479","DOIUrl":"https://doi.org/10.1109/TR.2025.3540479","url":null,"abstract":"Test case prioritization (TCP) has been widely used in software testing, which aims to execute test cases that are more likely to detect faults earlier than others. Among many proposed TCP approaches, lexicographical ordering-based TCP (LO-TCP) can effectively resolve ties encountered in the prioritization process, leading to better performance than original TCP approaches. However, the current LO-TCP needs to use the white-box information such as the code coverage of the program under test, which may be infeasible in some black-box testing applications such as software product lines (SPLs). In this article, we transfer the traditional LO-TCP to SPL testing by leveraging test configuration coverage instead of code coverage, and also empirically conduct some simulations and evaluate the large-scale real-world programs with real faults. The experimental results show that LO-TCP can have better performance for testing SPLs, as compared with traditional TCP approaches.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3326-3340"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anping Wan;Hua Zhang;Ting Chen;Khalil Al-Bukhaiti;Wenhui Wang;Jinglin Wang;Tianmin Shan;Luoke Hu
In this article, we introduce a novel method that uses enhanced sequential multitask learning to predict the remaining useful life (RUL) of an aeroengine accurately and efficiently while evaluating its status. The method innovatively employs extreme gradient boosting to extract critical performance parameters and construct a robust health indicator representing performance degradation. To capture the time-series features of the health indicator, the study modifies the traditional multigate mixture-of-experts (MMoE) model and integrates it with the gated recurrent unit (GRU) network, creating a hybrid MMoE-GRU model. In addition, we propose a dynamic weight balancing method to optimize the tradeoff in the joint loss function for multitask learning. Extensive experiments on the new commercial modular aero-propulsion system simulation (N-CMAPSS) dataset demonstrate that the proposed method significantly outperforms the existing models, achieving lower error rates and higher accuracy in RUL prediction and health status evaluation. The technique has a root-mean-square error (RMSE) of 7.1%, 6.9%, 1.3%, 0.6%, 1.7%, and 1.5% lower than the long short-term memory (LSTM), GRU, sequence-based bottom information gated recurrent unit (SB-GRU), deep gated recurrent unit (DGRU), multi-scale and multi-task convolutional neural network (M2STCNN), and MMoE models. The average accuracy of the proposed method is 96.732%, which is 10.629%, 1.587%, and 2.499% higher than those of the LSTM, DGRU, and MMoE, respectively. The superiority of the proposed method is validated on the N-CMAPSS dataset.
{"title":"Aeroengine Life Prediction and Status Evaluation Based on Sequential Multitask Learning and Health Indicators","authors":"Anping Wan;Hua Zhang;Ting Chen;Khalil Al-Bukhaiti;Wenhui Wang;Jinglin Wang;Tianmin Shan;Luoke Hu","doi":"10.1109/TR.2025.3535716","DOIUrl":"https://doi.org/10.1109/TR.2025.3535716","url":null,"abstract":"In this article, we introduce a novel method that uses enhanced sequential multitask learning to predict the remaining useful life (RUL) of an aeroengine accurately and efficiently while evaluating its status. The method innovatively employs extreme gradient boosting to extract critical performance parameters and construct a robust health indicator representing performance degradation. To capture the time-series features of the health indicator, the study modifies the traditional multigate mixture-of-experts (MMoE) model and integrates it with the gated recurrent unit (GRU) network, creating a hybrid MMoE-GRU model. In addition, we propose a dynamic weight balancing method to optimize the tradeoff in the joint loss function for multitask learning. Extensive experiments on the new commercial modular aero-propulsion system simulation (N-CMAPSS) dataset demonstrate that the proposed method significantly outperforms the existing models, achieving lower error rates and higher accuracy in RUL prediction and health status evaluation. The technique has a root-mean-square error (RMSE) of 7.1%, 6.9%, 1.3%, 0.6%, 1.7%, and 1.5% lower than the long short-term memory (LSTM), GRU, sequence-based bottom information gated recurrent unit (SB-GRU), deep gated recurrent unit (DGRU), multi-scale and multi-task convolutional neural network (M<sup>2</sup>STCNN), and MMoE models. The average accuracy of the proposed method is 96.732%, which is 10.629%, 1.587%, and 2.499% higher than those of the LSTM, DGRU, and MMoE, respectively. The superiority of the proposed method is validated on the N-CMAPSS dataset.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3833-3846"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhengkang Zuo;Yuhan Ke;Ying Hu;Qing Huang;Zhicheng Zeng;Changjing Wang
Network clustering (graph clustering) plays a crucial role in discovering the inherent structures within networks. MapReduce-based structural clustering algorithm for networks (MRSCANs) designed based on MapReduce's parallel computing model, efficiently handles large-scale data. However, MRSCAN can only be tested through experiments, and its correctness cannot be guaranteed. To address this issue, this article has achieved the first implementation of functional MRSCAN modeling and subjected it to rigorous mechanized verification in Isabelle. First, based on Google's MapReduce model type definition and higher-order generic functions, a general MapReduce-based algorithm functional modeling framework is constructed across the fundamental global phases of Map, Shuffle, and Reduce. Moreover, diverse strategies are devised during the Shuffle phases according to user requirements, enhancing the applicability and generality of the MapReduce functional modeling framework. Second, formalizing the definition of MRSCAN, is delineated into four key steps: similarity calculation, core calculation, dimension expansion, and structural clustering. Furthermore, the MapReduce functional modeling framework is applied to these four steps to achieve the functional modeling of MRSCAN, which improves efficiency compared to other structural clustering algorithm for networks (SCANs) algorithms. Lastly, a verification framework for MapReduce-based algorithms is proposed at both the global and shuffle stages. Based on this framework, the correctness and reliability of MRSCAN are ensured. The model framework and verification framework of MapReduce-based algorithms proposed in this article can not only address functional modeling and verification of MRSCAN but also provide a reference for a series of other MapReduce-based functional program designs and proofs.
{"title":"Modeling and Verification of MRSCAN Based on MapReduce Framework","authors":"Zhengkang Zuo;Yuhan Ke;Ying Hu;Qing Huang;Zhicheng Zeng;Changjing Wang","doi":"10.1109/TR.2025.3535754","DOIUrl":"https://doi.org/10.1109/TR.2025.3535754","url":null,"abstract":"Network clustering (graph clustering) plays a crucial role in discovering the inherent structures within networks. MapReduce-based structural clustering algorithm for networks (MRSCANs) designed based on MapReduce's parallel computing model, efficiently handles large-scale data. However, MRSCAN can only be tested through experiments, and its correctness cannot be guaranteed. To address this issue, this article has achieved the first implementation of functional MRSCAN modeling and subjected it to rigorous mechanized verification in Isabelle. First, based on Google's MapReduce model type definition and higher-order generic functions, a general MapReduce-based algorithm functional modeling framework is constructed across the fundamental global phases of Map, Shuffle, and Reduce. Moreover, diverse strategies are devised during the Shuffle phases according to user requirements, enhancing the applicability and generality of the MapReduce functional modeling framework. Second, formalizing the definition of MRSCAN, is delineated into four key steps: similarity calculation, core calculation, dimension expansion, and structural clustering. Furthermore, the MapReduce functional modeling framework is applied to these four steps to achieve the functional modeling of MRSCAN, which improves efficiency compared to other structural clustering algorithm for networks (SCANs) algorithms. Lastly, a verification framework for MapReduce-based algorithms is proposed at both the global and shuffle stages. Based on this framework, the correctness and reliability of MRSCAN are ensured. The model framework and verification framework of MapReduce-based algorithms proposed in this article can not only address functional modeling and verification of MRSCAN but also provide a reference for a series of other MapReduce-based functional program designs and proofs.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3311-3325"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdelhamid Boujarif;David W. Coit;Oualid Jouini;Zhiguo Zeng;Robert Heidsieck
In this article, we develop a data-driven approach to predict the reliability of multicomponent repairable systems, considering component dependencies. We estimate component reliability functions from system-level time-to-failure data without prior knowledge of the system structure and use these estimates to generate training data for a deep long short-term memory network. This leads to system reliability prediction and addresses uncertainties through quantile regression. Validated through simulations of 500 systems and real-world data from GE HealthCare magnetic resonance imaging (MRI) machines, our model outperforms traditional methods (such as Cox model and random survival forest) in terms of accuracy, particularly for complex systems, by effectively learning from uncertainties.
{"title":"A Deep-Learning-Based Framework to Predict the Reliability of Multicomponent Repairable Systems in a Closed-Loop Supply Chain","authors":"Abdelhamid Boujarif;David W. Coit;Oualid Jouini;Zhiguo Zeng;Robert Heidsieck","doi":"10.1109/TR.2025.3528074","DOIUrl":"https://doi.org/10.1109/TR.2025.3528074","url":null,"abstract":"In this article, we develop a data-driven approach to predict the reliability of multicomponent repairable systems, considering component dependencies. We estimate component reliability functions from system-level time-to-failure data without prior knowledge of the system structure and use these estimates to generate training data for a deep long short-term memory network. This leads to system reliability prediction and addresses uncertainties through quantile regression. Validated through simulations of 500 systems and real-world data from GE HealthCare magnetic resonance imaging (MRI) machines, our model outperforms traditional methods (such as Cox model and random survival forest) in terms of accuracy, particularly for complex systems, by effectively learning from uncertainties.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3809-3823"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Source code refactoring brings many benefits to the software being developed, e.g., reduces the likelihood of future development failures and simplifies the implementation of new features. Among the various code refactoring activities, identifier renaming is one of the most frequent software development activities conducted by developers, which plays an important role in program analysis and understanding. However, manually detecting identifier renaming opportunities is time-consuming and labor-intensive. Recently, researchers have proposed several automatic renaming opportunity identification approaches for identifiers. However, existing approaches only focus on one or several specific types of identifiers without generally considering all the types of identifiers. To resolve this problem, we put forward a new approach to detect identifier renaming opportunities by fully exploiting the changes of the programming context and the related code entities. Specifically, we first utilize a siamese network, which employs different attention headers to incorporate the programming context and the related code entities, to derive the semantically meaningful embeddings of identifiers. We then utilize these vectors to train a classifier, which can be used for predicting renaming opportunities for identifiers. Experimental results on 29 255 identifiers from ten Java projects in the Apache community demonstrate that our approach outperforms the state-of-the-art baseline approach by 11.97% as for the average F-Measure in identifying renaming opportunities for all the types of identifiers. In addition, we also verified the effectiveness of some key components of our approach. For instance, utilizing the related code entities into our approach improves the average F-Measure by 6.60%.
{"title":"Boosting Identifier Renaming Opportunity Identification via Context-Based Deep Code Representation","authors":"Jingxuan Zhang;Zhuhang Li;Jiahui Liang;Zhiqiu Huang","doi":"10.1109/TR.2025.3535736","DOIUrl":"https://doi.org/10.1109/TR.2025.3535736","url":null,"abstract":"Source code refactoring brings many benefits to the software being developed, e.g., reduces the likelihood of future development failures and simplifies the implementation of new features. Among the various code refactoring activities, identifier renaming is one of the most frequent software development activities conducted by developers, which plays an important role in program analysis and understanding. However, manually detecting identifier renaming opportunities is time-consuming and labor-intensive. Recently, researchers have proposed several automatic renaming opportunity identification approaches for identifiers. However, existing approaches only focus on one or several specific types of identifiers without generally considering all the types of identifiers. To resolve this problem, we put forward a new approach to detect identifier renaming opportunities by fully exploiting the changes of the programming context and the related code entities. Specifically, we first utilize a siamese network, which employs different attention headers to incorporate the programming context and the related code entities, to derive the semantically meaningful embeddings of identifiers. We then utilize these vectors to train a classifier, which can be used for predicting renaming opportunities for identifiers. Experimental results on 29 255 identifiers from ten Java projects in the Apache community demonstrate that our approach outperforms the state-of-the-art baseline approach by 11.97% as for the average F-Measure in identifying renaming opportunities for all the types of identifiers. In addition, we also verified the effectiveness of some key components of our approach. For instance, utilizing the related code entities into our approach improves the average F-Measure by 6.60%.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3296-3310"},"PeriodicalIF":5.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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