IEEE Transactions on Reliability最新文献_第6页

Bayesian Analysis of Accelerated Trend Renewal Processes With Application to Lithium-Ion Battery Data 加速趋势更新过程的贝叶斯分析及其在锂离子电池数据中的应用

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-01-15 DOI: 10.1109/TR.2024.3523180

Tsai-Hung Fan;Yi-Fu Wang;Chun-Kai Wu

{"title":"Bayesian Analysis of Accelerated Trend Renewal Processes With Application to Lithium-Ion Battery Data","authors":"Tsai-Hung Fan;Yi-Fu Wang;Chun-Kai Wu","doi":"10.1109/TR.2024.3523180","DOIUrl":"https://doi.org/10.1109/TR.2024.3523180","url":null,"abstract":"During battery reliability tests, quality characteristic (QC) values like capacitance, voltage, or current are repeatedly observed during the cyclic charge-discharge processes. The battery's lifetime is determined by the first cycle where QC values drop below a specific threshold. Despite the recurrent nature of this cyclic data, performance declines with each charge-discharge cycle. The trend renewal process (TRP) transforms this periodic data through a trend function to ensure independent and stationary increments in the transformed data. However, combining the trend function with the renewal distribution complicates the resulting likelihood function. In typical battery reliability tests, sample sizes are small, and batteries exhibit heterogeneous differences. This article examines the inverse Gaussian accelerated trend-renewal process (ATRP) model for analyzing discharge-capacity battery data under various discharge currents, with model parameters being log-linear in discharge current. A hierarchical Bayesian approach is employed for three ATRP random-effects models, introducing latent variables to capture unit-to-unit variation among batteries. By selecting the most appropriate model based on the largest log marginal likelihood, predictive lifetime inference under normal discharging current is derived using the Markov chain Monte Carlo procedure. Monte-Carlo simulations validate the numerical calculations, and the proposed method is successfully applied to lithium-ion battery accelerated degradation test data.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3083-3097"},"PeriodicalIF":5.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997993","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}

引用次数: 0

Collaborative Cloud-Controlled Defense Mechanism for Low-Carbon Economic Dispatch in Active Distribution Networks Under Interlayer Attack 层间攻击下主动配电网低碳经济调度协同云控防御机制

IF 5 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-01-14 DOI: 10.1109/TR.2024.3523894

Cong Cai;Yunfeng Wang;Qingyu Su;Jian Li

{"title":"Collaborative Cloud-Controlled Defense Mechanism for Low-Carbon Economic Dispatch in Active Distribution Networks Under Interlayer Attack","authors":"Cong Cai;Yunfeng Wang;Qingyu Su;Jian Li","doi":"10.1109/TR.2024.3523894","DOIUrl":"https://doi.org/10.1109/TR.2024.3523894","url":null,"abstract":"This article presents a collaborative cloud-based control and defense framework designed to address scheduling challenges and interlayer false data injection (FDI) attacks in a low carbon economy. The proposed framework integrates the principles of low carbon economy strategy and new energy (wind turbine, photovoltaic) modeling to coordinate active and reactive power of distributed generation (DG) using a layered control approach. The framework consists of two main layers: a lower layer and an upper layer. The lower layer combines control and attack defense strategies. State feedback control is utilized to regulate the dynamics of the DG and defense strategies are employed to defend against potential controller FDI attacks. The upper layer, on the other hand, consists of interlayer defense strategies and cloud computing. The FDI defense from the lower control layer to the upper cloud computing layer obtains the actual operating state of the DG. And these data are used for cloud computing to get the next reference power. Cloud computing focuses on multiobjective optimization with the aim of minimizing generation cost, line loss, and bus voltage deviation under low carbon conditions. In order to verify the effectiveness of the proposed control strategy, simulations are conducted on a computer and StarSim hardware-in-the-loop experimental platform. The results show that the framework can effectively manage energy consumption in a low-carbon economy.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 2","pages":"2655-2667"},"PeriodicalIF":5.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205854","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}

引用次数: 0

Integrated Temperature and Stress Sensors in Fan-Out Wafer-Level Packaging to Better Achieve the Third-Generation Reliability of Electronic Systems 在扇出晶圆级封装中集成温度和应力传感器，以更好地实现电子系统的第三代可靠性

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-01-13 DOI: 10.1109/TR.2024.3523892

Linwei Cao;Yuexing Wang;Kun Liu;Xiangou Zhang;Shuairong Deng;Quanfeng Zhou;Xiangyu Sun;Wanli Zhang

{"title":"Integrated Temperature and Stress Sensors in Fan-Out Wafer-Level Packaging to Better Achieve the Third-Generation Reliability of Electronic Systems","authors":"Linwei Cao;Yuexing Wang;Kun Liu;Xiangou Zhang;Shuairong Deng;Quanfeng Zhou;Xiangyu Sun;Wanli Zhang","doi":"10.1109/TR.2024.3523892","DOIUrl":"https://doi.org/10.1109/TR.2024.3523892","url":null,"abstract":"To satisfy the developmental requirements of applications, such as autonomous driving, high-performance computing, and the Internet of Things (IoT), the integration density, performance, and reliability tradeoff of electronic systems are posing numerous challenges. Prognostics and health management (PHM) using multiple types of sensors can address reliability problems and enhance the functional safety of electronic systems. However, the limited integration density of conventional electronic packaging indicates that functional chips can only replace sensor chips for physical quantity monitoring, without simultaneous functional degradation monitoring and fault identification. This study proposed an integration method that is compatible with front and rear processes to integrate temperature and stress sensors into the power-driven module, that is, fan-out wafer-level packaging technology. First, the temperature and stress sensors are calibrated using a microloading platform and sensitivity consistency is ensured. Second, the temperature inside the module under various working conditions is evaluated using the data obtained by temperature sensors. The stress data inside the micromodule under mechanical loading are obtained through stress sensors. The proposed method can realize <italic>in situ monitoring inside advanced packaging and provide considerable data for PHM research.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4020-4031"},"PeriodicalIF":5.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997930","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}

引用次数: 0

Reliability Evaluation for a Circular Con/k/n:F System With a Novel Differential Repair Policy 一种新型差动维修策略的圆形Con/k/n:F系统可靠性评估

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-01-13 DOI: 10.1109/TR.2024.3524329

Shan Gao;Jinting Wang;Qin Chen

{"title":"Reliability Evaluation for a Circular Con/k/n:F System With a Novel Differential Repair Policy","authors":"Shan Gao;Jinting Wang;Qin Chen","doi":"10.1109/TR.2024.3524329","DOIUrl":"https://doi.org/10.1109/TR.2024.3524329","url":null,"abstract":"In many industrial environments, some components fail and cannot be repaired immediately. We propose a novel repair policy for addressing component failure issues in a circular consecutive-<inline-formula><tex-math>$k$</tex-math></inline-formula>-out-of-<inline-formula><tex-math>$n$</tex-math></inline-formula>:F (abbreviated as Cir/Con/k/n:F) system. This repair policy assigns preemptive priority for repair to the component whose breakdown results in system failure (called <italic>emergency repair), while renders an <italic>ordinary repair to the failed components without causing failure of the system. The ordinary repairs are recorded by the repairman in the order of their failure, which is said that the broken components are stored in “orbit.” When the repairman becomes idle, he makes the orbital search for failed ones according to the first-failed-first-repair discipline, which can be interrupted by an emergency repair. We carry on an extensive investigation on reliability and queueing indices of the considered model. Specifically, we present a Cir/Con/2/6:F system as an example to give sensitivity analysis for the reliability performance. Numerical inversion of Laplace transform–Stehfest method is adopted to obtain approximate solutions for reliability function. Furthermore, the minimization problem of the CBR is addressed by adopting sequential quadratic programming algorithm. This study offers new insights into balancing the expected total repair cost and associated benefits in the Cir/Con/k/n:F system.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3098-3111"},"PeriodicalIF":5.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998380","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}

引用次数: 0

Semantic Structure Invariance-Based Metamorphic Testing for Machine Translation Systems 基于语义结构不变性的机器翻译系统变形测试

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-01-07 DOI: 10.1109/TR.2024.3521029

Chang-ai Sun;Jian Mu;Mingjun Xiao;Huai Liu;Pinjia He

{"title":"Semantic Structure Invariance-Based Metamorphic Testing for Machine Translation Systems","authors":"Chang-ai Sun;Jian Mu;Mingjun Xiao;Huai Liu;Pinjia He","doi":"10.1109/TR.2024.3521029","DOIUrl":"https://doi.org/10.1109/TR.2024.3521029","url":null,"abstract":"In recent years, deep neural networks have been applied in machine translation systems, resulting in the so-called neural machine translation (NMT) models that can improve translation quality significantly. However, due to the brittleness of deep neural network, machine translation systems could return erroneous translations that lead to misunderstandings or even cause serious losses. To detect translation errors, various testing techniques have been proposed. As a popularly used technique, metamorphic testing mainly relies on text or syntactic structure of translations while ignoring the meaning of sentences (i.e., semantic information). Compared with text and syntactic information, semantic information of sentences is more stable when dealing with languages that have rich vocabulary and flexible word order. Motivated by this observation, we propose semantic structure invariance-based metamorphic testing (SSIMT) for machine translation systems. The key insight is that contextually similar sentences should typically have translations of similar semantic structures. Experiments have been conducted to evaluate SSIMT on two widely used machine translation systems, Microsoft Bing Translator and Google Translate with 600 seed sentences crawled from well-known news websites covering six different corpus topics. The experimental results show that SSIMT is able to find thousands of erroneous translations in both translation systems with high accuracy (over 70%). Translation errors reported by SSIMT covers a wide variety of common error types.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3251-3265"},"PeriodicalIF":5.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998325","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}

引用次数: 0

Enhancing Java Web Application Security: Injection Vulnerability Detection via Interprocedural Analysis and Deep Learning 增强Java Web应用程序安全性：通过过程间分析和深度学习检测注入漏洞

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2025-01-07 DOI: 10.1109/TR.2024.3521381

Bing Zhang;Xu Zhi;Meng Wang;Rong Ren;Jun Dong

{"title":"Enhancing Java Web Application Security: Injection Vulnerability Detection via Interprocedural Analysis and Deep Learning","authors":"Bing Zhang;Xu Zhi;Meng Wang;Rong Ren;Jun Dong","doi":"10.1109/TR.2024.3521381","DOIUrl":"https://doi.org/10.1109/TR.2024.3521381","url":null,"abstract":"Injection attacks exploit vulnerabilities in how applications handle user input, allowing malicious code to infiltrate the execution environment of web applications, leading to severe consequences, such as data leaks and system crashes. Traditional dynamic and static detection methods suffer from limitations in manual rule or pattern design and intraprocedural analysis, lacking the capability to automatically learn complex features. Meanwhile, deep learning models encounter challenges, such as feature redundancy and inefficiency, in processing long code sequences. Here, we propose a prototype for detecting <underline>Injection <underline>Vulnerabilities in Java web applications based on <underline>Interprocedural analysis and the bidirectional encoder representations from transformers <underline>BERT-BiLSTM-CRF model (IVIB), effectively transforming vulnerability detection into text sequence annotation. IVIB employs interprocedural analysis to trace complete program data flow, control flow, method and class dependencies, reducing redundancy through a system dependency graph. Then, we develop intermediate language representation rules and conversion mechanisms specifically for Java programs, symbolically representing code snippets and annotating them to construct a corpus. IVIB achieves remarkable results, with over 96% accuracy, precision, recall, and F1-score in binary classification, surpassing other state-of-the-art models in multiclassification performance. Evaluation on real-world projects demonstrates IVIB's effectiveness, detecting 28 vulnerabilities out of 30 vulnerable slices with low false positives and no false negatives.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3642-3656"},"PeriodicalIF":5.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998326","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}

引用次数: 0

Reliability Assessment of Reconfigurable k-out-of-n Systems With Functional Dependency 具有功能依赖的可重构k-out- n系统可靠性评估

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-27 DOI: 10.1109/TR.2024.3507363

Yi-Xuan Zheng;Boyuan Zhang;Yu Liu

{"title":"Reliability Assessment of Reconfigurable k-out-of-n Systems With Functional Dependency","authors":"Yi-Xuan Zheng;Boyuan Zhang;Yu Liu","doi":"10.1109/TR.2024.3507363","DOIUrl":"https://doi.org/10.1109/TR.2024.3507363","url":null,"abstract":"The <italic>k-out-of-<italic>n system with functional dependency (FDEP), as a typical structure, has widespread applications in a diversity of engineered system. These systems are characterized by components that perform distinct functions, and are connected through flexible intercomponential support relations. This flexibility allows for dynamic adjustment of the support strategy in response to component failures, achieved through connections between components’ interfaces or controlled by additional components, such as valves and switches. Even though previous article has demonstrated effectiveness in assessing reliability of <italic>k-out-of-<italic>n systems with FDEP, it often overlooks the essential investigation of flexible support relations among components, resulting in inaccurate system reliability assessment. To fill this research gap, this article introduces a novel framework that integrates a parameter time-varying discrete dynamic Bayesian network (PTVDDBN) and a tailored Hungarian algorithm with a depth-first search (DFS) strategy, namely the PTVDDBN–HDFS method, to advance reliability assessment of <italic>k-out-of-<italic>n systems with flexible support relations. Specifically, the PTVDDBN-based architecture captures the system's stochastic degradation over time, and its components’ lifetime could follow an arbitrary probability distribution. From a graph set-based perspective, the support strategy designated in the system is dynamically adjusted via the DFS strategy. The optimal system performance under various component state combinations is further converted to conditional probability table parameters within the PTVDDBN model. A practical case study of a kerosene filling system at a space launch site is showcased to illustrate the application and effectiveness of the PTVDDBN–HDFS method.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3745-3759"},"PeriodicalIF":5.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998328","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}

引用次数: 0

Predictive and Multigranularity Resilience Assessment of Urban Transportation Based on Neural Controlled Differential Equation 基于神经控制微分方程的城市交通弹性预测多粒度评价

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-27 DOI: 10.1109/TR.2024.3514712

Zhe Cui;Di Zang;Hong Zhu;Keshuang Tang

{"title":"Predictive and Multigranularity Resilience Assessment of Urban Transportation Based on Neural Controlled Differential Equation","authors":"Zhe Cui;Di Zang;Hong Zhu;Keshuang Tang","doi":"10.1109/TR.2024.3514712","DOIUrl":"https://doi.org/10.1109/TR.2024.3514712","url":null,"abstract":"Crafting a dynamic and accurate resilience assessment method for urban transportation, marked by complex road networks and frequent disturbances, poses a significant challenge. Existing work mainly focuses on statically assessing historical traffic resilience and cannot dynamically divide spatial regions according to disturbance scales. In this article, we propose a predictive and multigranularity assessment method. First, we develop an attention-based spatial-temporal hypergraph neural controlled differential equation model, which can accurately predict traffic conditions under disturbances. Second, we construct a multigranularity disturbance propagation model that adaptively divides a traffic network into multiple granularities according to disturbance scales. Then, we design a real-time resilience assessment algorithm capable of quantifying spatial-temporal dynamic resilience indicators for each granularity area. Extensive experiments on urban transportation in California during heavy rainfall reveal an inverse relationship between California's resilience and rainfall intensity. In addition, its downtown exhibits strong resilience, while coastal and interior areas show relatively weaker resilience, with some interior areas experiencing prolonged recovery times.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4230-4244"},"PeriodicalIF":5.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998081","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}

引用次数: 0

Optimizing Power Resilience Performance of Intelligent Solar Photovoltaic System for Smart Energy Management Considering Reliability and Cost 考虑可靠性和成本的智能太阳能光伏系统电力弹性性能优化

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-26 DOI: 10.1109/TR.2024.3517312

Hongyan Dui;Yaohui Lu;Liudong Xing

{"title":"Optimizing Power Resilience Performance of Intelligent Solar Photovoltaic System for Smart Energy Management Considering Reliability and Cost","authors":"Hongyan Dui;Yaohui Lu;Liudong Xing","doi":"10.1109/TR.2024.3517312","DOIUrl":"https://doi.org/10.1109/TR.2024.3517312","url":null,"abstract":"Due to being nonpolluting and renewable, intelligent solar photovoltaic (PV) technology is widely used to provide electricity and becomes a cornerstone to sustainable energy and smart energy management. Different from existing studies that improve the PV efficiency by changing cell materials, this article proposes a novel system reliability and cost model of enhancing the PV power resilience performance from the perspective of optimizing the number of PV panels. Specifically, a multiobjective planning model is proposed, which determines the optimum number of spare parts for PV panels maximizing the output power resilience while maximizing the system reliability and minimizing the cost. The reliability measures the probability of stable operation of a PV panel considering the no-power output state. The cost factor encompasses negative cost of environmental benefits, resource cost, operation and maintenance cost, and penalty cost. Experiments are performed on fifty sets of Pareto optimal solutions in summer and winter cases to illustrate effectiveness of the proposed method by using a ground-mounted PV project in Zhongwei City, China.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3071-3082"},"PeriodicalIF":5.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998052","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}

引用次数: 0

Stacked Ensemble Deep Learning for the Classification of Nonfunctional Requirements 面向非功能需求分类的堆叠集成深度学习

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-25 DOI: 10.1109/TR.2024.3513834

Ayah Alqurashi;Luay Alawneh

{"title":"Stacked Ensemble Deep Learning for the Classification of Nonfunctional Requirements","authors":"Ayah Alqurashi;Luay Alawneh","doi":"10.1109/TR.2024.3513834","DOIUrl":"https://doi.org/10.1109/TR.2024.3513834","url":null,"abstract":"Requirements engineering is the foundation for software quality. Defining the correct software requirements in the initial phases of the software development life cycle minimizes project costs and efforts. While functional requirements (FRs) define the software features, nonfunctional requirements (NFRs), such as availability, performance, security, and reliability are essential for the acceptance and deployment of the software. Understanding software requirements from different stakeholders is a tedious task. Manual investigation of the stakeholder needs may skip important NFRs. Thus, the need for automatic requirements classification techniques arose to eliminate the misinterpretation of stakeholder needs and to speed up the development process. Several machine learning approaches targeted the classification of NFRs. We explore the recurrent neural network, long short-term memory, and gated recurrent unit deep learning (DL) methods. We apply the random search technique for hyperparameter optimization. Further, we use stacked ensemble learning to enhance the classification by combining the strengths of the base models using support vector machine as a meta-learner. We use grid search to optimize the hyperparameters of the meta-learner. Further, we compare the stacked ensemble approach with the BERT language model. The proposed approach is evaluated on 914 NFRs gathered from two datasets. Our ensemble model achieved a weighted average precision, recall, and F1-Score of 0.91, 0.90, 0.90, respectively.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3221-3235"},"PeriodicalIF":5.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998350","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}

引用次数: 0