IEEE Transactions on Reliability最新文献

{"title":"Editorial: AI and ML for Resilience Assessment and Enhancement of Cyber-Physical Systems","authors":"Winston Shieh","doi":"10.1109/TR.2024.3499412","DOIUrl":"https://doi.org/10.1109/TR.2024.3499412","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"1709-1709"},"PeriodicalIF":5.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777520","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}

{"title":"Corrections to “Guest Editorial: Crisis Management—From Nuclear Accidents to Outbreaks of COVID-19 and Infectious Diseases”","authors":"Way Kuo;Jufang He","doi":"10.1109/TR.2020.3036771","DOIUrl":"https://doi.org/10.1109/TR.2020.3036771","url":null,"abstract":"In [1], in Fig. 2 of the original version of the editorial, the relationship between the impact on gross domestic product (GDP) and the severity of these casualties is plotted for different events. As the SARS data used in the editorial were from, as well as related to China, it was labeled “China, SARS,” under the data point. Since the information on disease naming has been provided by the World Health Organization, it is more appropriate to label the data point as “SARS in China” in Fig. 2.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"1993-1993"},"PeriodicalIF":5.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777779","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}

{"title":"IEEE Reliability Society Information","authors":"","doi":"10.1109/TR.2024.3501175","DOIUrl":"https://doi.org/10.1109/TR.2024.3501175","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"C2-C2"},"PeriodicalIF":5.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777521","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}

{"title":"Erratum to “Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks”","authors":"Sen Fang;You-shuai Tan;Tao Zhang;Zhou Xu;Hui Liu","doi":"10.1109/TR.2021.3088600","DOIUrl":"https://doi.org/10.1109/TR.2021.3088600","url":null,"abstract":"In [1], the affiliation of the primary authors should be as follows:","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"1994-1994"},"PeriodicalIF":5.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777519","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}

{"title":"CFG2AT: Control Flow Graph and Graph Attention Network-Based Software Defect Prediction","authors":"Haiyang Liu;Zhiqiang Li;Hongyu Zhang;Xiao-Yuan Jing;Jinhui Liu","doi":"10.1109/TR.2024.3503688","DOIUrl":"https://doi.org/10.1109/TR.2024.3503688","url":null,"abstract":"Software defect prediction (SDP) plays a pivotal role in ensuring high-quality software development by aiding in the early identification of potential defects. This practice has gained substantial attention in the field of software engineering over the years. Recent advancements in deep learning have primarily focused on extracting general syntactic features from abstract syntax trees (ASTs) for SDP. However, AST-based neural network models might overlook important structural information related to control flows embedded within the source code. Given that software defects are often influenced by control flow patterns, this article proposes a novel SDP approach called control flow graph and graph attention (CFG2AT) network-based SDP. CFG2AT is specifically designed to automatically identify software defects and contains a graph-structured attention unit to effectively capture control flow information. To evaluate the effectiveness of CFG2AT, we carried out extensive experiments using data from 15 versions of six different open-source software projects under both within-project and cross-project defect prediction settings. Experimental results demonstrate that our proposed CFG2AT approach generally outperforms a range of competing methods for defect prediction. The improvement is 7.09%–12.80% in <italic>F1</i>, 1.30%–4.15% in area under curve (AUC), and 6.78%–17.54% in Matthews correlation coefficient (MCC) under within-project defect prediction, and 23.76%–44.79% in <italic>F1</i>, 8.93%–13.27% in AUC, and 36.92%–94.89% in MCC under CPDP, respectively.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3412-3426"},"PeriodicalIF":5.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998219","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}

{"title":"BiCAM: A Bidirectional Contextualized Attentive Model for Analyzing the Correlation of Heterogeneous Security Events","authors":"Xi Luo;Junhui Wang;Lihua Yin;Kaiyan Zhao;Kexiang Qian;Daojuan Zhang;Kai Chen","doi":"10.1109/TR.2024.3491894","DOIUrl":"https://doi.org/10.1109/TR.2024.3491894","url":null,"abstract":"As the Internet continues to evolve, modern information technology infrastructures are constantly under attack and need to be continuously monitored for timely responses. Different devices and detection platforms generate heterogeneous security events that are sent to security operations centers, where security operators investigate those events and identify potential threats. Unfortunately, it is impossible to manually analyze such a huge number of events, leading to “alert fatigue.” Despite a substantial amount of effort having been made to aggregate redundant related alerts, the effectiveness of previous works was essentially restrained by their limited relation learning and explaining abilities. In this work, we propose the bidirectional contextualized attentive model (BiCAM), a novel contextual analysis model that uses a self-supervised deep learning approach to automatically correlate security events in relation to their bidirectional context. It is developed by designing an encoder–decoder architecture that consists of bidirectional gated recurrent units and an attention mechanism to capture both sequential and nonsequential relations of previous and subsequent alerts and provide explainability information for the security operators. In addition, we introduce a bidirectional encoder representations from transformers (BERT)-based embedding method to deal with the heterogeneity of security events, enhancing our model's accommodation to the changes of detectors. We comprehensively evaluate our model on real-world datasets containing over 11M events generated by detectors from 8 different vendors. We found that our model enables accurate, unsupervised correlation extraction; and outperforms the state-of-the-art (SOTA) work when applying event relevance to semiautomatically classify security events (e.g., the <inline-formula><tex-math>$F1$</tex-math></inline-formula>-score of classification is improved by 4.3% and the false positive rate dropped to 1.39%).","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 2","pages":"2640-2654"},"PeriodicalIF":5.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10777841","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206118","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}

{"title":"MetaMFL: Metamorphic Multiple Fault Localization Without Test Oracles","authors":"Lingfeng Fu;Zhenyu Wu;Yan Lei;Meng Yan","doi":"10.1109/TR.2024.3504400","DOIUrl":"https://doi.org/10.1109/TR.2024.3504400","url":null,"abstract":"Multiple fault localization (MFL) identifies the positions of multiple faults (i.e., more than one fault) residing in a buggy program. It is notably more difficult as compared with single fault localization (SFL) which aims to locate a single fault (i.e., one fault) in a buggy program. Clustering-based multiple fault localization (CBMFL) is amongst the most popular MFL approaches, showing promising results in multiple fault localization. The requisite of launching CBMFL depends on test oracles to acquire the test results (i.e., a pass or a failure). In practice, test oracles are commonly not available known as the oracle problem, and CBMFL becomes infeasible in these cases. Inspired by metamorphic testing in solving the oracle problem, we attempt to combine this technique into CBMFL to broaden its application scope. Thus, we propose MetaMFL: <underline>Meta</u>morphic <underline>M</u>ultiple <underline>F</u>ault <underline>L</u>ocalization, which leverages metamorphic testing to extend CBMFL to the cases where test oracles are not available. Specifically, MetaMFL uses metamorphic testing groups as minimum units of testing. It defines metamorphic features for representing those that have violated metamorphic relations. Using these features, CBMFL can perform clustering to support parallel debugging, thus achieving MFL without test oracles. The large-scale experiments show that MetaMFL largely retains the effectiveness of CBMFL even though test oracles are not available.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3236-3250"},"PeriodicalIF":5.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996097","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}

{"title":"What Causes Bugs in Numerical Simulation Software? An Empirical Study","authors":"Xiaochen Li;Youcheng Zhu;Shikai Guo;He Jiang","doi":"10.1109/TR.2024.3492380","DOIUrl":"https://doi.org/10.1109/TR.2024.3492380","url":null,"abstract":"Numerical simulation (NS) software is widely used in safety-critical domains (e.g., aerospace design) to simulate actual physical processes of real-world entities on computers. However, NS software is error-prone, whose bugs lead to incorrect simulation, and may even cause disastrous flaws in safety-critical applications. Although many studies investigate the bug characteristics of computation-centered software, such as machine learning systems, the characteristics of NS software bugs have not been fully studied: what are the root causes and symptoms; how are they different from other computation-centered software; and why the difference occurs. To bridge this gap, we present a systematic study of NS software bugs by analyzing 352 bugs in three popular NS projects (i.e., FDS, SU2, and Kratos) for different domains. We summarize seven root causes (with 18 subcategories) and five symptoms. We find that the correctness, completeness, compatibility, and parallelization to implement NS algorithms (i.e., models) are error-prone. Many root causes (e.g., incorrect model and incorrect initialization) require physical and chemical knowledge to avoid bugs, which may not be mastered by typical software developers. These findings motivate new challenges and opportunities for future NS software development, such as designing domain specific language systems for NS model.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3383-3397"},"PeriodicalIF":5.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998196","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}

{"title":"Semisupervised Health Index Monitoring With Feature Generation and Fusion","authors":"Gaëtan Frusque;Ismail Nejjar;Majid Nabavi;Olga Fink","doi":"10.1109/TR.2024.3496076","DOIUrl":"https://doi.org/10.1109/TR.2024.3496076","url":null,"abstract":"The health index (HI) is crucial for evaluating system health, important for tasks, such as anomaly detection and remaining useful life prediction of safety-critical systems. Real-time, meticulous monitoring of system conditions is essential, especially in manufacturing high-quality and safety-critical components, such as spray coating. However, acquiring accurate health status information (HI labels) in real scenarios can be difficult or costly because it requires continuous, precise measurements that fully capture the system's health. As a result, using datasets from systems run-to-failure, which provide limited HI labels at just the healthy and end-of-life phases, becomes a practical approach. We employ the deep semisupervised anomaly detection (DeepSAD) embeddings to tackle the challenge of extracting features associated with the system's health state In addition, we introduce a diversity loss to further enrich the DeepSAD embeddings. We also propose applying an alternating projection algorithm with isotonic constraints to transform the embedding into a normalized HI with an increasing trend. Validation on the PHME2010 milling dataset, a recognized benchmark with ground truth HIs, confirms the efficacy of our proposed HIs estimations. Our methodology is further applied to monitor wear states of thermal spray coatings using high-frequency voltage. Our contributions facilitate more accessible and reliable HI estimation, particularly in scenarios where obtaining ground truth HI labels is impossible.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4005-4019"},"PeriodicalIF":5.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998029","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}

{"title":"Two-Dimensional Optimization Framework of Online Interpretable Time-Frequency Feature Learning for Practical Machine Health Monitoring","authors":"Tongtong Yan;Dong Wang;Tangbin Xia;Lifeng Xi;Min Xia","doi":"10.1109/TR.2024.3489589","DOIUrl":"https://doi.org/10.1109/TR.2024.3489589","url":null,"abstract":"Data-driven feature extraction for machine health monitoring has garnered significant attention, yet two key limitations remain unaddressed: lack of interpretability and the need for extensive historical fault data. To overcome these problems, an online two-dimensional optimization framework is proposed that enables interpretable time-frequency feature extraction and health index (HI) construction without requiring faulty samples for model training. Our approach introduces a convex hull-based closest point optimization model for estimating time-frequency instances and learning interpretable time-frequency features. By leveraging a small set of baseline vibration samples and recent online data, rapid fault diagnosis can be achieved based on optimized interpretable time-frequency features. This method also facilitates long-term degradation tracking by constructing and updating an HI from collected time-frequency spectrograms. Once machine faults appear, updated time-frequency features can show apparent and interpretable fault signatures for prompt fault alarming. Moreover, the proposed framework allows continuous HI updates for incipient fault detection and degradation tracking. The proposed framework is validated by using two run-to-failure datasets and ablation experiments are conducted to demonstrate its superiority.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3990-4004"},"PeriodicalIF":5.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998349","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}