IET Software最新文献

{"title":"Exploiting DBSCAN and Combination Strategy to Prioritize the Test Suite in Regression Testing","authors":"Zikang Zhang,&nbsp;Jinfu Chen,&nbsp;Yuechao Gu,&nbsp;Zhehao Li,&nbsp;Rexford Nii Ayitey Sosu","doi":"10.1049/2024/9942959","DOIUrl":"https://doi.org/10.1049/2024/9942959","url":null,"abstract":"<div>\u0000 <p>Test case prioritization techniques improve the fault detection rate by adjusting the execution sequence of test cases. For static black-box test case prioritization techniques, existing methods generally improve the fault detection rate by increasing the early diversity of execution sequences based on string distance differences. However, such methods have a high time overhead and are less stable. This paper proposes a novel test case prioritization method (DC-TCP) based on density-based spatial clustering of applications with noise (DBSCAN) and combination policies. By introducing a combination strategy to model the inputs to generate a mapping model, the test inputs are mapped to consistent types to improve generality. The DBSCAN method is then used to refine the classification of test cases further, and finally, the Firefly search strategy is introduced to improve the effectiveness of sequence merging. Extensive experimental results demonstrate that the proposed DC-TCP method outperforms other methods in terms of the average percentage of faults detected and exhibits advantages in terms of time efficiency when compared to several existing static black-box sorting methods.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9942959","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Expository Examination of Temporally Evolving Graph-Based Approaches for the Visual Investigation of Autonomous Driving","authors":"Li Wan,&nbsp;Wenzhi Cheng","doi":"10.1049/2024/5802816","DOIUrl":"10.1049/2024/5802816","url":null,"abstract":"<div>\u0000 <p>With the continuous advancement of autonomous driving technology, visual analysis techniques have emerged as a prominent research topic. The data generated by autonomous driving is large-scale and time-varying, yet more than existing visual analytics methods are required to deal with such complex data effectively. Time-varying diagrams can be used to model and visualize the dynamic relationships in various complex systems and can visually describe the data trends in autonomous driving systems. To this end, this paper introduces a time-varying graph-based method for visual analysis in autonomous driving. The proposed method employs a graph structure to represent the relative positional relationships between the target and obstacle interferences. By incorporating the time dimension, a time-varying graph model is constructed. The method explores the characteristic changes of nodes in the graph at different time instances, establishing feature expressions that differentiate target and obstacle motion patterns. The analysis demonstrates that the feature vector centrality in the time-varying graph effectively captures the distinctions in motion patterns between targets and obstacles. These features can be utilized for accurate target and obstacle recognition, achieving high recognition accuracy. To evaluate the proposed time-varying graph-based visual analytic autopilot method, a comparative study is conducted against traditional visual analytic methods such as the frame differencing method and advanced visual analytic methods like visual lidar odometry and mapping. Robustness, accuracy, and resource consumption experiments are performed using the publicly available KITTI dataset to analyze and compare the three methods. The experimental results show that the proposed time-varying graph-based method exhibits superior accuracy and robustness. This study offers valuable insights and solution ideas for developing deep integration between intelligent networked vehicles and intelligent transportation. It provides a reference for advancing intelligent transportation systems and their integration with autonomous driving technologies.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5802816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Cross-Project Defect Prediction Using Transfer Learning with Long Short-Term Memory Networks","authors":"Hongwei Tao,&nbsp;Lianyou Fu,&nbsp;Qiaoling Cao,&nbsp;Xiaoxu Niu,&nbsp;Haoran Chen,&nbsp;Songtao Shang,&nbsp;Yang Xian","doi":"10.1049/2024/5550801","DOIUrl":"10.1049/2024/5550801","url":null,"abstract":"<div>\u0000 <p>With the increasing number of software projects, within-project defect prediction (WPDP) has already been unable to meet the demand, and cross-project defect prediction (CPDP) is playing an increasingly significant role in the area of software engineering. The classic CPDP methods mainly concentrated on applying metric features to predict defects. However, these approaches failed to consider the rich semantic information, which usually contains the relationship between software defects and context. Since traditional methods are unable to exploit this characteristic, their performance is often unsatisfactory. In this paper, a transfer long short-term memory (TLSTM) network model is first proposed. Transfer semantic features are extracted by adding a transfer learning algorithm to the long short-term memory (LSTM) network. Then, the traditional metric features and semantic features are combined for CPDP. First, the abstract syntax trees (AST) are generated based on the source codes. Second, the AST node contents are converted into integer vectors as inputs to the TLSTM model. Then, the semantic features of the program can be extracted by TLSTM. On the other hand, transferable metric features are extracted by transfer component analysis (TCA). Finally, the semantic features and metric features are combined and input into the logical regression (LR) classifier for training. The presented TLSTM model performs better on the <i>f</i>-measure indicator than other machine and deep learning models, according to the outcomes of several open-source projects of the PROMISE repository. The TLSTM model built with a single feature achieves 0.7% and 2.1% improvement on Log4j-1.2 and Xalan-2.7, respectively. When using combined features to train the prediction model, we call this model a transfer long short-term memory for defect prediction (DPTLSTM). DPTLSTM achieves a 2.9% and 5% improvement on Synapse-1.2 and Xerces-1.4.4, respectively. Both prove the superiority of the proposed model on the CPDP task. This is because LSTM capture long-term dependencies in sequence data and extract features that contain source code structure and context information. It can be concluded that: (1) the TLSTM model has the advantage of preserving information, which can better retain the semantic features related to software defects; (2) compared with the CPDP model trained with traditional metric features, the performance of the model can validly enhance by combining semantic features and metric features.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5550801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media","authors":"Yuanyuan Fan,&nbsp;Xifeng Mi","doi":"10.1049/2024/6819714","DOIUrl":"https://doi.org/10.1049/2024/6819714","url":null,"abstract":"<div>\u0000 <p>In the rapidly evolving landscape of social media, the demand for precise sentiment analysis (SA) on multimodal data has become increasingly pivotal. This paper introduces a sophisticated data lake architecture tailored for efficient multimodal emotion feature extraction, addressing the challenges posed by diverse data types. The proposed framework encompasses a robust storage solution and an innovative SA model, multilevel spatial attention fusion (MLSAF), adept at handling text and visual data concurrently. The data lake architecture comprises five layers, facilitating real-time and offline data collection, storage, processing, standardized interface services, and data mining analysis. The MLSAF model, integrated into the data lake architecture, utilizes a novel approach to SA. It employs a text-guided spatial attention mechanism, fusing textual and visual features to discern subtle emotional interplays. The model’s end-to-end learning approach and attention modules contribute to its efficacy in capturing nuanced sentiment expressions. Empirical evaluations on established multimodal sentiment datasets, MVSA-Single and MVSA-Multi, validate the proposed methodology’s effectiveness. Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. This research significantly contributes to optimizing SA in social media data by offering a versatile and potent framework for data management and analysis. The integration of MLSAF with a scalable data lake architecture presents a strategic innovation poised to navigate the evolving complexities of social media data analytics.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6819714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Unveiling the Dynamics of Extrinsic Motivations in Shaping Future Experts’ Contributions to Developer Q&A Communities","authors":"Yi Yang,&nbsp;Xinjun Mao,&nbsp;Menghan Wu","doi":"10.1049/2024/8354862","DOIUrl":"10.1049/2024/8354862","url":null,"abstract":"<div>\u0000 <p>Developer question and answering communities rely on experts to provide helpful answers. However, these communities face a shortage of experts. To cultivate more experts, the community needs to quantify and analyze the rules of the influence of extrinsic motivations on the ongoing contributions of those developers who can become experts in the future (potential experts). Currently, there is a lack of potential expert-centred research on community incentives. To address this gap, we propose a motivational impact model with self-determination theory-based hypotheses to explore the impact of five extrinsic motivations (badge, status, learning, reputation, and reciprocity) for potential experts. We develop a status-based timeline partitioning method to count information on the sustained contributions of potential experts from Stack Overflow data and propose a multifactor assessment model to examine the motivational impact model to determine the relationship between potential experts’ extrinsic motivations and sustained contributions. Our results show that (i) badge and reciprocity promote the continuous contributions of potential experts while reputation and status reduce their contributions; (ii) status significantly affects the impact of reciprocity on potential experts’ contributions; (iii) the difference in the influence of extrinsic motivations on potential experts and active developers lies in the influence of reputation, learning, and status and its moderating effect. Based on these findings, we recommend that community managers identify potential experts early and optimize reputation and status incentives to incubate more experts.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/8354862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139853919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"A Meta-Model Architecture and Elimination Method for Uncertainty Modeling","authors":"Haoran Shi,&nbsp;Shijun Liu,&nbsp;Li Pan","doi":"10.1049/2024/5591449","DOIUrl":"10.1049/2024/5591449","url":null,"abstract":"<div>\u0000 <p>Uncertainty exists widely in various fields, especially in industrial manufacturing. From traditional manufacturing to intelligent manufacturing, uncertainty always exists in the manufacturing process. With the integration of rapidly developing intelligent technology, the complexity of manufacturing scenarios is increasing, and the postdecision method cannot fully meet the needs of the high reliability of the process. It is necessary to research the pre-elimination of uncertainty to ensure the reliability of process execution. Here, we analyze the sources and characteristics of uncertainty in manufacturing scenarios and propose a meta-model architecture and uncertainty quantification (UQ) framework for uncertainty modeling. On the one hand, our approach involves the creation of a meta-model structure that incorporates various strategies for uncertainty elimination (UE). On the other hand, we develop a comprehensive UQ framework that utilizes quantified metrics and outcomes to bolster the UE process. Finally, a deterministic model is constructed to guide and drive the process execution, which can achieve the purpose of controlling the uncertainty in advance and ensuring the reliability of the process. In addition, two typical manufacturing process scenarios are modeled, and quantitative experiments are conducted on a simulated production line and open-source data sets, respectively, to illustrate the idea and feasibility of the proposed approach. The proposed UE approach, which innovatively combines the domain modeling from the software engineering field and the probability-based UQ method, can be used as a general tool to guide the reliable execution of the process.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2024 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5591449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139624985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis","authors":"Rexford Nii Ayitey Sosu,&nbsp;Jinfu Chen,&nbsp;Edward Kwadwo Boahen,&nbsp;Zikang Zhang","doi":"10.1049/2023/6631967","DOIUrl":"10.1049/2023/6631967","url":null,"abstract":"<div>\u0000 <p>Smart contracts have gained immense popularity in recent years as self-executing programs that operate on a blockchain. However, they are not immune to security flaws, which can result in significant financial losses. These flaws can be detected using dynamic analysis methods that extract various aspects from smart contract bytecode. Methods currently used for identifying vulnerabilities in smart contracts mostly rely on static analysis methods that search for predefined vulnerability patterns. However, these patterns often fail to capture complex vulnerabilities, leading to a high rate of false negatives. To overcome this limitation, researchers have explored machine learning-based methods. However, the accurate interpretation of complex logic and structural information in smart contract code remains a challenge. In this study, we present a technique that combines real-time runtime batch normalization and data augmentation for data preprocessing, along with n-grams and one-hot encoding for feature extraction of opcode sequence information from the bytecode. We then combined bidirectional long short-term memory (BiLSTM), convolutional neural network, and the attention mechanism for vulnerability detection and classification. Additionally, our model includes a gated recurrent units memory module that enhances efficiency using historical execution data from the contract. Our results demonstrate that our proposed model effectively identifies smart contract vulnerabilities.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2023 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/6631967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment","authors":"Mohammed Naif Alatawi,&nbsp;Saleh Alyahyan,&nbsp;Shariq Hussain,&nbsp;Abdullah Alshammari,&nbsp;Abdullah A. Aldaeej,&nbsp;Ibrahim Khalil Alali,&nbsp;Hathal Salamah Alwageed","doi":"10.1049/2023/4324783","DOIUrl":"10.1049/2023/4324783","url":null,"abstract":"<div>\u0000 <p>In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2023 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/4324783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Observational Study on React Native (RN) Questions on Stack Overflow (SO)","authors":"Luluh Albesher,&nbsp;Razan Aldossari,&nbsp;Reem Alfayez","doi":"10.1049/2023/6613434","DOIUrl":"10.1049/2023/6613434","url":null,"abstract":"<div>\u0000 <p>Mobile applications are continuously increasing in prevalence. One of the main challenges in mobile application development is creating cross-platform applications. To facilitate developing cross-platform applications, the software engineering community created several solutions, one of which is React Native (RN), which is a popular cross-platform framework. The software engineering literature demonstrated the effectiveness of Stack Overflow (SO) in providing real-world perspectives on a variety of technical subjects. Therefore, this study aims to gain a better understanding of the stance of RN on SO. We identified and analyzed 131,620 SO RN-related questions. Moreover, we observed how the interest toward RN on SO evolves over time. Additionally, we utilized Latent Dirichlet Allocation (LDA) to identify RN-related topics that are discussed within the questions. Afterward, we utilized a number of proxy measures to estimate the popularity and difficulty of these topics. The results revealed that interest toward RN on SO was generally increasing. Moreover, RN-related questions revolve around six topics, with the topics of layout and navigation being the most popular and the topic of iOS issues being the most difficult. Software engineering researchers, practitioners, educators, and RN contributors may find the results of this study beneficial in guiding their future RN efforts.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2023 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/6613434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139199214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Analysis of Emotional Deconstruction and the Role of Emotional Value for Learners in Animation Works Based on Digital Multimedia Technology","authors":"Shilei Liang","doi":"10.1049/2023/5566781","DOIUrl":"10.1049/2023/5566781","url":null,"abstract":"<div>\u0000 <p>With the rapid development of artificial intelligence and digital media technology, modern animation technology has greatly improved the creative efficiency of creators through computer-generated graphics, electronic manual painting, and other means, and its number has also experienced explosive growth. The intelligent completion of emotional expression identification within animation works holds immense significance for both animation production learners and the creation of intelligent animation works. Consequently, emotion recognition has emerged as a focal point of research attention. This paper focuses on the analysis of emotional states in animation works. First, by analyzing the characteristics of emotional expression in animation, the model data foundation for using sound and video information is determined. Subsequently, we perform individual feature extraction for these two types of information using gated recurrent unit (GRU). Finally, we employ a multiattention mechanism to fuse the multimodal information derived from audio and video sources. The experimental outcomes demonstrate that the proposed method framework attains a recognition accuracy exceeding 90% for the three distinct emotional categories. Remarkably, the recognition rate for negative emotions reaches an impressive 94.7%, significantly surpassing the performance of single-modal approaches and other feature fusion methods. This research presents invaluable insights for the training of multimedia animation production professionals, empowering them to better grasp the nuances of emotion transfer within animation and, thereby, realize productions of elevated quality, which will greatly improve the market operational efficiency of animation industry.</p>\u0000 </div>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2023 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2023/5566781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}