IET Software最新文献

Though ensuring logical consistency between flows in use case specifications (UCSs) and high-level business processes is a prerequisite and foundation for generating high-quality, full-coverage test cases, this task still primarily relies on manual effort. To address this limitation, a large language model (LLM)-assisted test-driven approach is proposed, which introduces a formal structure to guide LLMs in generating UCSs based on natural language requirements. Specifically, to validate the consistency of UCS flows with high-level business process logic, UML activity and state machine diagrams are used as specific modeling methods for such processes. Furthermore, three novel rules are proposed to validate the consistency: business objects are incorporated to not only horizontally connect UCSs with these models but also vertically bridge different abstraction levels of requirements. To normalize this methodology, a comprehensive framework is established—this framework enables bidirectional traceability between requirements and testing while simultaneously building a feedback loop that integrates requirements, testing, and process models. Experimental results show that the approach not only improves the requirements specification quality but also facilitates semi-automatic generation of test cases for acceptance testing, thereby enhancing the efficiency of the overall development process.

Quantum software engineering is advancing rapidly in parallel with equally ambitious hardware roadmaps. However, systematic evidence on how online audiences perceive these advances remains scarce. We present an exploratory baseline of Twitter sentiment toward quantum computing, using automated (silver-standard) labels for benchmarking. Six months of English-language tweets containing the hashtag #Quantum (December 1, 2022 and May 31, 2023) were processed, with #Quantum treated as a proxy for online discourse on quantum computing. We then applied a transparent natural language processing (NLP) methodology combining two zero-shot lexicon-based tools (TextBlob and the Valence Aware Dictionary and sEntiment Reasoner [VADER]) with three lightweight supervised classifiers (multinomial naïve Bayes, Rocchio, and perceptron). Following standard preprocessing and a stratified 70/30 train–test split, we do not aim to measure definitive public opinion; rather, our primary contribution is to establish a transparent and reproducible baseline for future benchmarking. In this context, the multinomial naïve Bayes classifier attained a macro F1-score of 0.88 on the 30% hold-out set when benchmarked against the TextBlob silver labels. This score captures internal agreement rather than accuracy against human annotation. All five methods converged on a largely—though not universally—positive sentiment orientation (≈78%–81% of nonneutral tweets, depending on the tool). Grounded in the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), we interpret our results as indicating the constructs of curiosity and perceived usefulness, rather than unequivocal adoption readiness. These constructs were not operationalized and serve only as interpretative lenses. By documenting every preprocessing step and model configuration, and making tweet identifiers and code available upon request, the study delivers a reproducible benchmark against which future work can (i) extend the query vocabulary, (ii) incorporate neutral and fine-grained emotions, (iii) apply cross-validation protocols, and (iv) evaluate advanced transformer models on manually annotated data. Addressing these four points is essential before making any definitive claims about public opinion.

Recently, optical privacy protection has emerged as a promising approach for safeguarding visual privacy at the physical acquisition stage. However, existing methods often face a trade-off between privacy strength and human pose recognition accuracy, particularly in long-range and multi-scale scenarios. To address this challenge, we propose a novel adaptive optical privacy-preserving framework that integrates a learnable optical modulation system with a human pose recognition network. The core of our method lies in a sparse-weighted multi-lens model, where a lightweight multilayer perceptron (MLP) predicts a sparse set of coefficients to linearly combine predefined lens phase profiles based on facial region geometry. This enables dynamic control over the point spread function (PSF), adapting the degree of image degradation to subject scale in real time. Additionally, we introduce a privacy-aware loss function that selectively reduces facial localization accuracy while preserving body pose information. Extensive experiments on MSCOCO and FLIC datasets demonstrate that the proposed method achieves a favorable balance between privacy protection and pose estimation, outperforming previous optical- and software-based baselines.

The structural features of a code section that may indicate a more serious issue with the design of a system or code are known as code smells. Design patterns, on the other hand, are meant to describe the best reusable solution for creating object-oriented software systems. Even though design patterns and code smells are very different, they may co-occur. In fact, there may be a significant connection among the two, which requires further research. This study aims to (i) identify design patterns and code smells in web gaming code, (ii) investigate the co-occurrence of the two, and (iii) analyze the effects of these co-occurrences on internal quality aspects of code. An experiment is carried out on JavaScript (JS) web games utilizing machine learning classifiers to investigate the influence of co-occurrence on potential code smells and design patterns to evaluate games from a quality perspective. Moreover, statistical testing is performed to identify the impact of co-occurrences of code smells and design patterns on internal quality attributes. After examining the data, we determined that random forest is the most effective classifier, achieving an accuracy of 99.126% and 98.99% for both experimental situations, respectively. Moreover, on applying the Wilcoxon signed rank test, we found that co-occurrence has no impact on the coupling and complexity of web games codes, whereas there is a significant impact of co-occurrence on cohesion, size, and inheritance. Our results may guide developers in writing efficient games code to add to this swiftly growing market.

We present a permissioned blockchain–audited federated learning (FL) framework that strengthens data provenance and model-update integrity. Our contribution is primarily engineering and architectural: a modular two-channel design (provenance vs. update-audit), lightweight on-chain validation with off-chain analytics, and a practical mapping to the 1 + 5 architectural views. In a TensorFlow Federated + Hyperledger Fabric prototype with 10 clients, we observe ≈18% faster anomaly detection under attack and a + 0.4 pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof-of-concept zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK) flow to validate per-client summary properties off-chain while anchoring results on-chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.

In the context of intensified market competition, consumer demand for aesthetically pleasing and functionally designed products has grown significantly. Color matching in product appearance plays a critical role in influencing consumer choice. However, designers often face challenges related to low color recognition accuracy, which hampers efficiency and design quality. This study explores the application of artificial intelligence (AI) technology to assist in the color matching process of product appearance and functional design. Experimental evaluation across various product types demonstrates that AI integration improves color accuracy by 2.09% and enhances the stability of image representation by 3.47%. Additionally, it reduces design analysis time, increases designer productivity, and boosts satisfaction scores by 5.4%. The findings confirm that AI technology effectively supports designers in achieving more accurate, efficient, and satisfactory color matching outcomes.

With the increasing size and complexity of software code, hidden defects can pose serious problems to systems, making zero-defect software an urgent need for current industrial software applications. Software defect prediction (SDP) serves to identify defective modules or classes, with prediction models trained using historical defect data from various projects. This enables defect prediction in test projects, aiding in the rational allocation of test resources and the enhancement of software quality. The efficacy of SDP closely hinges on the quality of the defect dataset, the selected metric index, the trained model, and the algorithm design. This article reviews recent literature on SDP, summarizing existing research from three key perspectives: the dataset and metric elements employed in SDP, dataset optimization processing techniques, and defect prediction model techniques. It primarily focuses on introducing commonly used datasets and two types of defect metrics for SDP. Regarding dataset optimization processing technology, it discusses methods for handling abnormal data, high-dimensional data, class imbalance data, and data disparity issues. Furthermore, it analyzes the construction of prediction models across four dimensions: supervised learning, semi-supervised learning, unsupervised learning, and deep learning (DL). Key observations include: (i) Researchers utilize datasets of varying quality, performance evaluation metrics, and SDP models. The efficacy of software product metrics and development process metrics varies across different application scenarios, necessitating flexible metric selection based on actual requirements. (ii) Commonly used datasets like Promise and NASA exhibit varying data quality. Appropriate data preprocessing methods and dataset creation are crucial before training SDP models. (iii) In scenarios with limited labeled data, cross-project transfer learning, semi-supervised, or unsupervised learning methods tend to better utilize a broader range of training data. Given that each step in the SDP process corresponds to different unresolved issues, each requiring varying levels of response measures, we suggest that researchers comprehensively consider research objectives such as dataset quality, SDP model, performance evaluation indicators, and the need for model interpretability when conducting SDP-related research. It’s important to note that no universal dataset or model can perform optimally across different application scenarios.

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.

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 Q-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.