Multimodal remote sensing image classification has emerged as a key research area in remote sensing, with extensive applications in real-world scenarios. However, these images are collected by different sensors and contain multiple features such as spectrum, space, height and texture. Due to the differences in the characteristics of these data, existing methods have poor results in extracting and fusing heterogeneous features, which limits the improvement of classification performance. To address this problem, we propose a new heterogeneous feature extraction and fusion framework DTFNet, which utilizes the diffusion model and Transformer architecture. In the feature extraction stage, different networks are constructed to extract heterogeneous features while reducing redundancy. The dual-branch diffusion feature extraction (DBDFE) network based on the diffusion model is introduced to process data from different sensors, avoiding the limitation of extracting all features with a single network. In the feature fusion stage, the extracted diffusion features are fused with the original features to preserve the integrity of the original data. The cross-fusion transformer (CFT) module uses a convolutional neural network (CNN) to complete the local feature transformation and integration and models the long-range dependencies between heterogeneous features through cross-transformer encoders. Experimental results show that the classification accuracy of DTFNet on the three datasets reaches 92.38%, 80.08% and 95.02% respectively, which is significantly better than the existing state-of-the-art methods, demonstrating its effectiveness and superiority.

The rapid development of Large Language Models (LLMs), such as ChatGPT and DeepSeek, has revolutionized software development, particularly in the domain of automated code generation. These models, built on architectures like the Transformer, have demonstrated remarkable capabilities in generating human-like text and source code, significantly enhancing developer productivity and reducing development time. However, the widespread adoption of LLMs for code generation raises concerns regarding the reliability, quality, and potential risks associated with the generated code. This article illustrates and analyzes the state of the art in evaluating LLM-generated code, summarizing research findings, and application areas. This paper highlights the challenges in distinguishing between machine-generated and human-written code, as well as the potential for LLMs to introduce security vulnerabilities and maintainability issues. We discuss the implications of these findings for both researchers and practitioners, emphasizing the need for continued research in the evaluation of LLM-generated code. Finally, we identify gaps in the literature and propose future research directions, such as the development of more robust benchmarks and improved evaluation metrics. By providing a thorough overview of the current landscape, this paper provides a valuable resource for researchers and practitioners interested in LLM’s code generation capabilities and limitations. We also highlight the importance of ongoing evaluation and refinement of these models to ensure their safe and effective integration into software-development practices.

Large language models (LLMs) have rapidly become a powerful tool in automated code generation, yet most research has focused on their correctness and efficiency rather than the stylistic patterns of their outputs. In this preliminary study, we analyze the code patterns generated by five popular LLMs—ChatGPT, Gemini, Claude, Grok, and DeepSeek—in their free versions, across three LeetCode problems, one top-ranking each from the easy, medium, and hard categories. Our evaluation employs key metrics including inline comment density, naming conventions, and edge case handling, highlighting both similarities and differences in verbosity, comprehensibility, and robustness among the codes generated by models. The findings of this study have important implications for software engineering and education, suggesting that LLM-generated code can serve as both a tool for rapid prototyping and an effective learning resource for beginners. Our future work will extend this analysis to a broader set of coding challenges and compare LLM outputs with human-written code to develop robust criteria for evaluating automated code generation.

The increasing complexity of deep learning models, especially in black-box scenarios, presents significant challenges to traditional software testing methods. Due to the lack of transparency in neural networks’ decision-making processes and the non-deterministic nature of model outputs, traditional test oracle approaches become inadequate. To address this problem, Metamorphic Testing (MT) and its extended approach, Metamorphic Exploration (ME), provide new ideas for validating deep learning systems by defining Metamorphic Relations (MR) between inputs and outputs. However, existing image transformation-based MR faces new challenges in image synthesis scenarios, as these operations may destroy the contextual information and affect the model’s performance. This paper proposes a novel ME design for deep learning image synthesis networks and demonstrates its effectiveness using a visible-infrared image fusion network as the case study. The result identifies the performance degradation problem due to the tensor dimension manipulation error, which indicates that the ME not only detects defects but also helps developers deeply understand the internal mechanisms of complex systems through the Hypothesized Metamorphic Relation (HMR), thus providing unique value for software quality assurance (SQA) of AI-driven software.

Context: The rapid development of Machine Learning (ML) has led to the proposal of various ML models to improve credit score assessment, creating a need for effective validation methods to ensure their performance aligns with business expectations. Objective: This paper introduces a novel approach for validating credit scoring models by focusing on user-hypothesized business expectations, enabling testers to predict how input changes affect outputs and assess alignment with business intuition. Methods: The approach uses Metamorphic Testing (MT), applying Metamorphic Relations (MRs) to examine input–output relationships, and Metamorphic Exploration (ME), an advanced extension of MT that constructs MRs based on user expectations. A case study evaluates and contrasts three popular ML models, neural networks, random forests, and gradient boosting tree, using both traditional evaluation metrics in credit scoring and ME. The study investigates how models selected based on traditional metrics perform when evaluated against MRs. Results: Empirical findings reveal that all three models often violate MRs, with violations becoming more extensive as model complexity increases. Neural networks have low number of MR violations on average but tends to be less robust. Interestingly, random forests exhibit most MR violations relative to the other two models. Traditional metrics fail to capture these violations, highlighting their limitations in ensuring alignment with business expectations. Conclusions: ME is proposed as a complementary validation method for model selection and post-deployment monitoring, ensuring models adhere to business intuition. The study underscores the importance of combining traditional metrics with ME, particularly for complex models like neural networks, to improve reliability in real-world applications.

ABSTRACT Metamorphic testing (MT) is effective in detecting software failures; it detects failures by examining the metamorphic relations (MRs) among source test cases (STCs), follow‐up test cases (FTCs) and their respective outputs. The STCs together with the corresponding FTCs, considered as a whole, are called metamorphic groups (MGs). MT performance relies heavily on the MRs and MGs. Previous studies have mainly focused on improving MT performance by identifying effective MRs, or through generation of MGs with high quality, but have somewhat neglected the selection of MRs and MGs from existing ones. In this paper, we address this issue by introducing a new metric for guiding the selection of effective MR‐MG pairs from a new perspective: The MR‐MG pair is chosen such that the MR makes the current MG as far away as possible from the executed MGs. We design an MR‐MG pair selection algorithm, named metamorphic relation and group selection based on adaptive random testing (MRGS‐ART), to implement our metric. The intuition behind MRGS‐ART is that we attempt to improve MT performance by achieving an even distribution of STCs and FTCs in their corresponding input domains for all the MRs used. Experimental results indicate that MRGS‐ART can enhance MT performance. We believe that this is the first comprehensive and systematic demonstration, from the perspective of both MRs and MGs, that making STCs and FTCs evenly distributed in their corresponding input domains can improve MT performance. Finally, by analysing the experimental results, we provide guidance on how to most effectively implement MRGS‐ART.

Metamorphic Testing (MT) has been repeatedly proven effective in detecting software faults. MT detects faults by checking the Metamorphic Relations (MRs) among Source Test Cases (STCs) and Follow-up Test Cases (FTCs) and the corresponding outputs. Metamorphic Groups (MGs) denote the associated STCs and FTCs. The performance of MT relates strongly to the MRs and MGs. However, previous studies that on MG generation mainly focused on improving the effectiveness (i.e. fault-detection capability) of MT, but to some extent overlooked the efficiency. This paper proposes a new kind of MG generation algorithms called Metamorphic-Testing-based Adaptive Random Testing through Partitioning (MT-PART). These algorithms at-tempt to improve both the effectiveness and the efficiency of MT by dynamically partitioning the input domain and generating new STCs and FTCs that are uniformly distributed over their corresponding input domains. Through empirical experiments, we found that our algorithms are able to significantly outper-form other existing MG generation algorithms in terms of test efficiency, while maintaining good test effectiveness.

The performance of metamorphic testing relates strongly to the quality of test cases. However, most related research has only focused on source test cases, ignoring follow-up test cases to some extent. In this paper, we identify a potential problem that may be encountered with existing metamorphic group generation algorithms. We then propose a possible solution to address this problem. Based on this solution, we design a new algorithm for generating effective source and follow-up test cases. To improve the performance (test effectiveness and efficiency) of metamorphic testing. We introduce the concept of the input-domain difference problem, which is likely to affect the performance of metamorphic group generation algorithms. We propose a new test-case distribution criterion for metamorphic testing to address this problem. Based on our proposed criterion, we further present a new metamorphic group generation algorithm, from a black-box perspective, with new distance metrics to facilitate this algorithm. Our algorithm performs significantly better than existing algorithms, in terms of test effectiveness, efficiency and test-case diversity. Through experiments, we find that the input-domain difference problem is likely to affect the performance of metamorphic group generation algorithms. The experimental results demonstrate that our algorithm can achieve good test efficiency, effectiveness, and test-case diversity.

Metamorphic testing (MT) has been growing in pop-ularity, but it can still be quite challenging and time-consuming to assess its performance. Typical approaches to performance assessment can require a series of steps, and depend on a variety of factors, often requiring serendipity. This can be a bottleneck for some aspects of MT research. Central to MT, metamorphic relations (MRs) represent necessary properties of the system under test (SUT). In traditional software testing, simulations are often employed to examine and compare the performance of dif-ferent testing strategies. However, these simulations are typically designed based on the assumed availability (and applicability) of a test oracle - a mechanism to decide the correctness of the SUT output or behaviour. A key reason for the popularity of MT is its proven record of effective software testing, without the need for a test oracle. This strength, however, also means that traditional ways of using simulations to analyse software testing approaches are not applicable for MT. This lack of cheap and fast ways to conduct simulation analyses of MT is a hurdle for many aspects of MT research, and may be an obstacle to its more widespread adoption. To address this, in this paper we introduce the concept of MR-violation regions (MRVRs), and show how they can be used for a certain category of MRs, Deterministic MRs (DMRs), to build simulation tools for MT. We analyse the differences between MRVRs and traditional, oracle-defined failure regions; and report on a preliminary case study exploring MRVRs in numerical-input-domain systems from previous MT studies. We anticipate that the proposed MT simulation framework may facilitate more research into MT, and may help lead to its more widespread adoption.

The dual-function radar communication system develops rapidly with the integration of sensing function and communication function, the combination of vehicle tracking and positioning and vehicle communication leads to a more efficient vehicle networking system in the future. This paper proposes a beam tracking prediction scheme for intergraded sensing and communications (ISAC) aided vehicle to infrastructure communications. In detail, we focus on the beam misalignment problem between roadside units (RSU) and high dynamic passing vehicles. To solve this problem, we propose a particle filter-based predictive beamforming method that can predict the motion parameters of vehicles by using transmitted ISAC signals and received the vehicle echoes. The simulation results show that the proposed particle filter algorithm can reduce the overhead and predict the vehicle motion parameters and the vehicle's angle relative to the RSU when the vehicle is moving.