Accurate software project effort estimation remains a persistent challenge due to the limitations of traditional models in capturing rich contextual information from project artifacts. Conventional approaches such as COCOMO II and machine learning models primarily rely on structured numerical features, often ignoring the semantic depth present in Software Requirements Specification (SRS) documents. This gap leads to suboptimal estimation performance, particularly in early project stages. To address this limitation, this paper proposes a novel framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for context-aware software effort estimation. The proposed system leverages SRS documents to extract structured attributes, converts historical project data into semantic embeddings, and retrieves similar past projects using vector similarity search. These retrieved analogues are then utilized by a reasoning-based LLM to generate effort predictions grounded in both contextual and historical evidence. The framework is evaluated on the ISBSG dataset using both traditional error metrics and RAG-specific evaluation measures. Experimental results demonstrate that the proposed approach achieves an MMRE of 10.74%, outperforming baseline models including COCOMO II, Random Forest, and Support Vector Regression under identical preprocessing settings. Additional analysis using RAGAS metrics shows high context precision (0.9760) and competitive answer correctness (0.7020), validating the effectiveness of the retrieval-generation pipeline. The results highlight the potential of combining LLMs with retrieval mechanisms to bridge the gap between unstructured requirements and quantitative estimation, offering a scalable and interpretable solution for modern software project planning.
Adversarial attacks on machine learning-based malware detection models could enable attackers to craft evasive malware variants that remain functional while bypassing detection. However, existing methods primarily focus on feature-space adversarial attacks, lacking effective mapping from feature-space to code-space (i.e. the modifications of source code of malware to implement the adversarial attacks), which limits its practical application. To address this issue, this paper proposes an large language model (LLM) enhanced code-space adversarial attack method against the dynamic malware detection models, achieving end-to-end generation of executable and source code-level adversarial samples. First, we apply a feature-space adversarial attack algorithm to perturb the feature vectors and pinpoint the changes of dynamic behaviors (e.g. API calls). Second, we propose a prompt mechanism guided by the dynamic behavior changes and code generation hints to automatically modify the source code of malware samples by leveraging an LLM. Experiments on five types of typical malware demonstrate that the proposed method can effectively modify the source code of malware based on the adversarial perturbations, and consistently outperforms existing approaches in terms of evasion capability and functionality retention.
Software projects frequently report requirements-related failures even when systems conform to documented requirements and pass acceptance criteria. This paper argues that such failures stem from a conceptual conflation of requirements with technical specifications. We propose a socio-technical, contract-based conceptualization of software requirements, framing them as negotiated commitments defined by assumptions, constraints, and accountability structures. Drawing on requirements engineering and organizational theory, we analyze how requirements function as evolving agreements rather than static technical prescriptions. We identify core elements of requirement contracts and show how misalignment arises when underlying assumptions shift while technical conformance is maintained. The proposed perspective clarifies recurring failure scenarios and provides a structured lens for requirements elicitation, documentation, change management, and traceability. Rather than introducing a new method, this work offers a conceptual model that integrates socio-technical considerations into requirements artifacts and supports improved alignment between stakeholder commitments and technical realization.
Falls in the older population constitute a significant public health issue, motivating the development of automated fall risk assessment methods. However, existing approaches based on wearable sensors or depth cameras are often limited by hardware cost and deployment complexity. To this end, we present DualFRA, a vision-based deep learning approach that leverages standard cameras and integrates complementary mobility information from Timed Up and Go (TUG) and Five-Times Sit-to-Stand (FTSTS) test videos for comprehensive fall risk assessment. DualFRA first applies 3D pose estimation to extract skeletal sequences from the two clinical tests. For each sequence, multiple complementary re``presentations are constructed, including a spatio-temporal skeletal graph and auxiliary views that emphasize joint configurations and motion dynamics. These representations are then processed by a spatio-temporal feature encoder combining self-attention-enhanced graph convolution and multi-scale temporal modeling to capture discriminative movement patterns. The resulting dual-stream features are integrated through cross-attention to produce a coherent semantic embedding for fall risk prediction. Experimental results averaged across multiple independent runs demonstrate that DualFRA attains accuracy and weighted-F1-scores of 87.90% and 87.83%, surpassing baselines with average performance gains of 2.92% and 2.97%, respectively. Ablation analyses further confirm the benefits of the dual-stream design.
For distributed drive electric vehicles, accurate estimation of the road friction coefficient is essential for torque allocation, stability control, and full utilization of tire force potential since each wheel is independently actuated and directly constrained by the tire-road interaction. To address this issue, a robust road friction coefficient estimation method based on a sliding mode observer (SMO) and a square-root cubature Kalman filter (SRCKF) is proposed. The SMO is first designed to estimate the longitudinal and lateral tire forces using measurable vehicle states, providing reliable force information under conditions of modeling uncertainties and external disturbances. Based on the estimated tire forces, the SRCKF is then employed to fuse nonlinear tire model information and obtain an accurate and numerically stable estimate of the road friction coefficient. The proposed method is evaluated through simulation studies under various driving conditions. At a vehicle speed of 20km/h, the friction coefficient estimation error converges from 0.01 to 0 within 1.3s; at 60km/h, the estimation error converges from 0.6 to 0 within only 0.005s. The simulation results demonstrate that the proposed estimator can accurately track changes in road friction and provide reliable inputs for motion control and torque distribution in distributed drive vehicles.
