IET Software最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"A Novel Facial Expression Recognition Approach Combining Canny Edge Detection and Convolutional Neural Networks","authors":"Jiao Ding,&nbsp;Li Yang,&nbsp;Tianfei Zhang,&nbsp;Songlin Zhang,&nbsp;Meiyu Liang","doi":"10.1049/sfw2/4943761","DOIUrl":"10.1049/sfw2/4943761","url":null,"abstract":"<p>Facial expression recognition (FER) remains challenging under pose, illumination, and occlusion. This work presents CCFER, a dual-stream framework that couples explicit edge maps with appearance features. Grayscale faces undergo morphological closing followed by opening (5 × 5), then Canny with locally adaptive thresholds to produce clean edges for an edge branch; both streams use Dual-Direction Attention Mixed Feature Networks (DDAMFN). Multilevel fusion employs adaptively spatial feature fusion (ASFF), followed by Efficient Local Attention (ELA) and multihead attention (MHAtt) before classification. CCFER attains 92.19% on RAF-DB, 91.24% on FERPlus, and 67.32% on AffectNet-7, matching or approaching the recent state of the art with balanced cross-dataset performance. Controlled ablations (parameter-matched single-stream, random-noise edges) confirm gains stem from semantic contours, and efficiency measurements show modest overhead in parameters, GFLOPs, and latency, supporting practical deployment.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/4943761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145470162","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":"ReinSeed: Reinforcement Fuzz Testing With Multiphase Seed Optimization for Autonomous Driving Systems","authors":"Qi Jin,&nbsp;Tingting Wu,&nbsp;Yunwei Dong,&nbsp;Zuohua Ding,&nbsp;Yongkui Xu","doi":"10.1049/sfw2/8657455","DOIUrl":"10.1049/sfw2/8657455","url":null,"abstract":"<p>Ensuring the safety of autonomous driving systems (ADSs) is essential, which requires effective testing methods to enhance system robustness. Fuzz testing (FT) is a widely used technique for uncovering software faults by generating test cases that trigger unexpected system behaviors. However, traditional FT in ADS suffers from significant limitations, including inefficient seed selection, low test case relevance, and inadequate exploration of diverse failure-inducing driving scenarios. Random fuzzing often yields redundant or ineffective cases, limiting the detection of safety-critical issues. To address these challenges, we propose ReinSeed, a reinforcement FT (RFT) framework that integrates three key phases: prefuzzing seed optimization, reinforcement learning (RL)–based scenario generation, and postfuzzing seed prioritization. We introduce a scenario complexity index to prioritize initial seeds before fuzzing. During fuzzing, we model the process as a Markov decision process (MDP) and apply <i>Q</i>-learning to generate scenarios with effective fuzzing action variations guided by driving behaviors, including undesired behaviors and trajectory coverage. To further improve testing effectiveness, we present a postfuzzing prioritization strategy that ranks fuzzed scenarios based on risk energy by incorporating control constraint violation analysis, safety-critical events, and risk-driven trajectory. Experimental results demonstrate that the unified framework—ReinSeed—significantly improves the detection of undesired behaviors, outperforming baseline methods across maps of varying complexity. Furthermore, the multiphase seed optimization showcases distinct contributions of scenario complexity, behavior-guided fuzzing, and risk energy in enhancing both the efficiency and effectiveness of discovering critical behaviors in ADS.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/8657455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469814","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148070193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Enhancing Software Engineering With AI: Innovations, Challenges, and Future Directions","authors":"Tahir Abbas,&nbsp;Shujaat Ali Rathore,&nbsp;Amira Turki,&nbsp;Sunawar Khan,&nbsp;Omar Alghushairy,&nbsp;Ali Daud","doi":"10.1049/sfw2/5691460","DOIUrl":"10.1049/sfw2/5691460","url":null,"abstract":"<p>Software engineering, along with the incorporation of Artificial Intelligence (AI), has emerged as a new technological vantage point that has permanently changed classical development practices and processes for any phase and aspect of the software lifecycle. In particular, this systematic literature review, which includes 135 peer-reviewed papers extracted from the years 2010 to 2025, follows PRISMA guidelines. It examines modern instances of AI-based requirements analysis, automated code transformation, predictive system modeling, proactive fault monitoring and detection, and advanced project guidance systems. Technologies can be powerful tools for increasing productivity and effectiveness and strengthening the quality of software development while making technology more complex—technologically, organizationally, and ethically. The generalization, explainability, privacy and algorithmic bias challenges of the model are discussed in detail. This paper shows how AI is helping companies to predict defects, automatically identify errors and optimize the software development. It also highlights the significant adoption barriers to these technologies for organizations. The review combines new industry research with existing practice to offer practical guidance on how these implementation challenges can be overcome and the ethical use of AI can be promoted. In contrast to existing reviews concentrating on isolated stages, the study offers an integrated review through life phases, distinctive ethical frameworks and a roadmap for adoption. Takeaway: Sustainable AI deployment in SE needs interdisciplinary collaboration, ethical oversight, and a mixture of guidelines to balance technology efficiency with responsibility. The paper highlights that interdisciplinary cooperation and ethical framings are requirements to integrate AI into software engineering in a sustainable, straightforward way. This review can be utilized as a guide for authors, scientists/practitioners, and policymakers in articulating the intellectual-practical gap.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/5691460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145406827","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":"Vehicle Object Detection Algorithm Based on Region of Interest–Convolutional Neural Network","authors":"Zhaosheng Xu,&nbsp;Zhongming Liao,&nbsp;Jianbang Liu,&nbsp;Xiaoyong Xiao,&nbsp;Zhongqi Xiang,&nbsp;Xiuhong Xu","doi":"10.1049/sfw2/7289732","DOIUrl":"10.1049/sfw2/7289732","url":null,"abstract":"<p>Traditional vehicle object detection faces problems such as low detection precision, high computational complexity, and poor performance in handling complex backgrounds. To address these challenges, this article adopts the simple linear iterative clustering (SLIC) algorithm for superpixel segmentation, generates candidate regions through selective search (SS), and uses the VGG16 deep convolutional neural network (CNN) for feature extraction, combined with a Softmax classifier for classification. Finally, the accuracy of vehicle detection boxes is improved by precisely adjusting the detection results through regional regression networks. In the training and testing of the model on large-scale datasets, the combination of transfer learning and data augmentation techniques improves the model’s robustness and generalization capabilities. The experimental results show that the F1-score of the model exceeds 0.95 in most vehicle categories, and the precision of the motorcycle detection reaches 0.978. The real-time performance test shows that with high-end graphics cards and optimization strategies, the model frame rate can reach 125 frames per second (FPS) and exhibits good robustness under complex lighting and weather conditions. Compared with the existing region of interest (ROI)–CNN-based method, the SLIC superpixel + SS candidate region generation strategy proposed in this paper significantly reduces the missed detection of small vehicles and improves the quality of candidate frames by maintaining target boundary information at the superpixel level and performing multilevel merging, thereby improving the recall rate of small targets. At the same time, the VGG16 combined with dilated convolution feature extraction scheme effectively retains the contextual information in occluded scenes by expanding the receptive field without reducing the resolution of the feature map, thereby enhancing the recognition stability of partially occluded vehicles. This proves that the model based on the ROI–CNN is effective in improving detection accuracy and real-time performance, showing its potential application value in applications such as intelligent transportation and autonomous driving.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/7289732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366677","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148088840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Unsupervised Person Reidentification Using Stripe-Driven Fusion Transformer Network","authors":"Zeyu Zang,&nbsp;Yang Liu,&nbsp;Shuang Liu,&nbsp;Zhong Zhang,&nbsp;Xinshan Zhu","doi":"10.1049/sfw2/6394038","DOIUrl":"10.1049/sfw2/6394038","url":null,"abstract":"<p>In recent years, some methods utilize a transformer as the backbone to model the long-range context dependencies, reflecting a prevailing trend in unsupervised person reidentification (Re-ID) tasks. However, they only explore the global information through interactive learning in the framework of the transformer, which ignores the learning of the part information in the interaction process for pedestrian images. In this study, we present a novel transformer network for unsupervised person Re-ID, a stripe-driven fusion transformer (SDFT), designed to simultaneously capture the global interaction and the part interaction when modeling the long-range context dependencies. Meanwhile, we present a stripe-driven regularization (SDR) to constrain the part aggregation features and the global features by considering the consistency principle from the aspects of the features and the clusters, aiming to improve the representational capacity of the features. Furthermore, to investigate the relationships between local regions of pedestrian images, we present a stripe-driven contrastive loss (SDCL) to learn discriminative part features from the perspectives of pedestrian identity and stripes. The proposed method has undergone extensive validations on publicly available unsupervised person Re-ID benchmarks, and the experimental results confirm its superiority and effectiveness.</p>","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/sfw2/6394038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366659","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":50378,"journal":{"name":"IET Software","volume":"2025 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148087972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}