Manually extracting device knowledge from dense hardware manuals for requirements elicitation is a timeconsuming and error-prone bottleneck in embedded systems development. While Large Language Models (LLMs) offer automation potential, their direct application is unreliable for this high-precision task due to factual inaccuracies and sensitivity to prompt engineering. To address this, we propose a novel hybrid method that synergizes parameter-efficient tuning with the reasoning power of LLMs. Instead of directly tuning the LLM, our approach uses soft prompts to efficiently optimize a lightweight sentence encoder, transforming it into a domainaware semantic retriever. This specialized retriever then generates superior, context-rich prompts for a frozen LLM. Our optimization strategy features a multi-task contrastive loss to discern fine-grained semantics and a clustering-based exemplar selection process to ensure prompt diversity and relevance. On a real-world dataset of devices, our method achieves F1-scores of 87.02% for entity extraction and 77.60% for relation extraction, significantly outperforming baseline LLMs and a strong, heavyweight, domain-specific pre-trained model (EquipBERT).
Ensuring the safety of autonomous driving systems (ADS) in a cost-effective and efficient manner remains a critical challenge. Existing law-guided scenario generation approaches are typically limited to a narrow subset of legal rules, resulting in insufficient scenario diversity, and search-based methods often struggle with large and sparse search spaces. To address these limitations, we propose SLaFE (Semantic Law Modeling and Fuzzing based on Cooperative Evolution), a novel traffic violation scenario generation framework designed to systematically evaluate the safety of ADS. SLaFE harnesses the reasoning capabilities of large language models (LLMs) to convert traffic laws into structured scenario constraints. These scenarios are then optimized via a cooperative evolutionary fuzzing algorithm that explores the parameter space to identify boundary cases likely to trigger abnormal ADS behaviors. We evaluate SLaFE on the Apollo platform within the LGSVL simulator using ten realworld traffic regulations. Experimental results show that SLaFE successfully triggered all 10 types of traffic law violations (10/10), outperforming the best existing method, VioHawk (9/10), while others detected no more than 3. Moreover, SLaFE achieved an average triggering time of 5.1 minutes per law type, significantly faster than VioHawk (9.0 minutes) and other baselines. These results highlight SLaFE’s effectiveness in discovering diverse and critical law-violating scenarios for ADS testing.
Recent studies have demonstrated outstanding capabilities of large language models (LLMs) in software engineering tasks, including code generation and comprehension. While LLMs have shown significant potential in assisting with coding, LLMs are vulnerable to adversarial attacks. In this paper, we investigate the vulnerability of LLMs to imperceptible attacks. This class of attacks manipulate source code at the character level, which renders the changes invisible to human reviewers yet effective in misleading LLMs’ behaviour. We devise these attacks into four distinct categories and analyse their impacts on code analysis and comprehension tasks. These four types of imperceptible character attacks include coding reordering, invisible coding characters, code deletions, and code homoglyphs. To assess the robustness of state-of-the-art LLMs, we present a systematic evaluation across multiple models using both perturbed and clean code snippets. Two evaluation metrics, model confidence using log probabilities of response and response correctness, are introduced. The results reveal that LLMs are susceptible to imperceptible coding perturbations, with varying degrees of degradation highlighted across different LLMs. Furthermore, we observe a consistent negative correlation between perturbation magnitude and model performance. These results highlight the urgent need for robust LLMs capable of manoeuvring behaviours under imperceptible adversarial conditions.
Driver fatigue detection is a core functional module in intelligent driver monitoring systems, and its performance directly affects the reliability and safety of Advanced Driver Assistance Systems (ADAS). Existing computer vision-based fatigue detection methods have issues such as redundant feature extraction and insufficient modeling of long-term temporal dependencies. In particular, when facial actions are highly similar to yawning, they are prone to causing misjudgments in decisionmaking. To address these challenges, this paper proposes a facial feature and attention-based temporal convolutional network (FATCN) for software engineering practice, it improves the performance by optimizing feature engineering. FA-TCN includes two core software modules: the first is the facial feature detection module (FFD-Module), it simplifies the input dimensions by extracting the temporal features of the eye aspect ratio (EAR) and mouth aspect ratio (MAR), enhances the robustness of the module in complex driving scenarios; the second is the facial action classification module (FAC-Module), it uses an attention-based temporal convolutional network (TCN) to optimize the longsequence feature modeling capability by parallelizing convolution operations. The experimental results show that, on the YawDD dataset, FA-TCN outperforms existing advanced methods and effectively enhances the recognition ability of intelligent driving software for subtle fatigue actions.
Floating-point arithmetic is widely used in high-precision computing fields such as national defense, aerospace, and finance. Different applications have varying requirements for floating-point computation precision, and how to maximize program performance while meeting precision requirements is a crucial challenge in high-performance program design. Mixed-precision technology is a common optimization strategy to address this issue, which involves using multiple precision types within the same program. However, most existing research on mixed precision tends to fall into local optima and fails to directly provide users with usable mixed-precision programs. Since genetic algorithms are optimization methods with global search capabilities, we propose a genetic algorithm-based mixed-precision tuning method and develop a tool called GATUNER. Specifically, GATUNER first analyzes the variables and statements of the program to construct a dependency graph of the program. Then, it uses the Leiden algorithm to detect community structures in the dependency graph, employs an improved genetic algorithm to search for mixed-precision configurations, and finally automatically generates the corresponding mixed-precision program. We evaluated GATUNER on 14 benchmark tests and application programs. The experimental results show that GATUNER can find valid mixed-precision configurations for 76.79% of the test programs, with an average performance improvement of 31.65% for the found mixed-precision configurations. Compared with HIFPTUNER, ADAPT, and CHEF-FP, the average performance improvement of the mixed-precision configurations found by GATUNER increased by 14.1%, 24.09%, and 15.18% respectively. In addition, compared with PESA, the average performance improvement of the mixed-precision configurations obtained by GATUNER increased by 18.67%.
