Large language models (LLMs) exhibit strong generative capabilities and have shown great potential in code generation. Existing chain-of-thought (CoT) prompting methods enhance model reasoning by eliciting intermediate steps, but suffer from two major limitations: First, their uniform application tends to induce overthinking on simple tasks. Second, they lack intention abstraction in code generation, such as explicitly modeling core algorithmic design and efficiency, leading models to focus on surface-level structures while neglecting the global problem objective. Inspired by the cognitive economy principle of engaging structured reasoning only when necessary to conserve cognitive resources, we propose RoutingGen, a novel difficulty-aware routing framework that dynamically adapts prompting strategies for code generation. For simple tasks, it adopts few-shot prompting; for more complex ones, it invokes a structured reasoning strategy, termed Intention Chain-of-Thought (ICoT), which we introduce to guide the model in capturing task intention, such as the core algorithmic logic and its time complexity. Experiments across three models and six standard code generation benchmarks show that RoutingGen achieves state-of-the-art performance in most settings, while reducing total token usage by 46.37% on average across settings. Furthermore, ICoT outperforms six existing prompting baselines on challenging benchmarks.

Li Huang, Zhongxin Liu, Yifan Wu, Tao Yin, Dong li, Jichao Bi, Nankun Mu, Hongyu Zhang, Meng Yan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Large Language Models (LLMs) specialized in code have demonstrated impressive capabilities in various programming tasks such as code generation. However, these models often generate vulnerable code due to inherent flaws in training datasets derived from large-scale, unfiltered open-source repositories. Existing methods like SVEN (prefix tuning) and CoSec (supervised co-decoding) attempt to address these risks but face challenges with transferability or inflexible security constraints. To mitigate these issues, we propose SCoDE, a two-stage approach for secure and functionally correct code generation. After an initial functional tuning phase, we integrate a plug-andplay security steering matrix at the model’s output embedding layer. This matrix can be transferred across models without modifying their original weights. During inference, we introduce a novel contrastive decoding mechanism that adaptively balances the base model’s functional logits with positive and negative security steering signals. Extensive experiments on 60 security scenarios and two standard benchmarks (HumanEval, MBPP) using StarCoder, Qwen2.5-Coder, and CodeLlama demonstrate that SCoDE enhances security while maintaining functional correctness. On average, SCoDE improves security by 28.09% over the original models, 12.07% over CoSec, and 4.82% over SVEN. For functional correctness, it achieves average gains of 55.03% on HumanEval and 41.81% on MBPP over the original models.

Code LLMs (CLLMs) are vulnerable to adversarial attacks, where semantically identical code mutations mislead models into incorrect predictions. To address this, adversarial training has been proposed, retraining models with adversarial examples generated by attack methods. Among various attack approaches, black-box methods have attracted increasing attention due to their flexibility and applicability. However, existing black-box attack methods face two key challenges: 1) vast mutation spaces limit attack efficiency and effectiveness, and 2) resource-intensive model queries constrain scalability. These challenges hinder the practicality of black-box attacks, especially under resource constraints, prompting the critical question:Can we enhance the efficiency of existing attack methods without compromising their effectiveness?To answer this, we conduct an empirical study using Explainable AI (XAI) techniques to investigate differences between adversarial and non-adversarial (failure) examples. After analyzing state-of-the-art attack methods against two CLLMs, we introduce the concept ofmodel attention deviation, which quantifies differences in the model’s focus between unmutated (original) and mutated code. Our findings reveal that adversarial examples exhibit significant attention deviations, with the direction of deviation critically affecting attack success. Building on these insights, we propose ADVSEL, an efficient adversarial attack framework comprising two proxy components: the Attention Proxy Model (APM), which quickly estimates attention deviations to filter unpromising mutations, and the Deviation Direction Proxy Model (DDPM), which assesses whether attention shifts lead toward incorrect predictions. By integrating these proxy models with existing attack methods, ADVSELeffectively prioritizes promising mutations, significantly improving attack efficiency. Experimental evaluations across five CLLMs, four downstream tasks, and three attack methods demonstrate that ADVSEL maintains comparable attack success rates (a slight ASR reduction of 0.62%–0.70%) while significantly reducing model queries (by 34.98%–42.91%) and runtime (by 20.84%–21.45%). Under resource constraints, ADVSEL consistently outperforms baselines, highlighting its practical advantage in cost-effective adversarial evaluation.

Deep code models are vulnerable to adversarial attacks, making it possible for semantically identical inputs to trigger different responses. Current black-box attack methods typically prioritize the impact of identifiers on the model based on custom importance scores or program context and incrementally replace identifiers to generate adversarial examples. However, these methods often fail to fully leverage feedback from failed attacks to guide subsequent attacks, resulting in problems such as local optima bias and efficiency dilemmas. In this paper, we introduce ITGen, a novel black-box adversarial example generation method that iteratively utilizes feedback from failed attacks to refine the generation process. It employs a bitvectorbased representation of code variants to mitigate local optima bias. By integrating these bit vectors with feedback from failed attacks, ITGen uses an enhanced Bayesian optimization framework to efficiently predict the most promising code variants, significantly reducing the search space and thus addressing the efficiency dilemma. We conducted experiments on a total of nine deep code models for both understanding and generation tasks, demonstrating ITGen's effectiveness and efficiency, as well as its ability to enhance model robustness through adversarial finetuning. For example, on average, ITGen improves the attack success rate by 47.98 % and 69.70 % over the state-of-the-art techniques (i.e., ALERT and BeamAttack), respectively.

One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, as traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation learning and limited sensitivity to anomalous dispersion in high-dimensional spaces. In this work, we conduct an empirical analysis showing that both normal and anomalous samples tend to scatter in high-dimensional space, especially anomalous samples are markedly more dispersed. We formalize this dispersion phenomenon as scattering, quantified by the mean pairwise distance among sample representations, and leverage it as an inductive signal to enhance spatio-temporal anomaly detection. Technically, we propose ScatterAD to model representation scattering across temporal and topological dimensions. ScatterAD incorporates a topological encoder for capturing graph-structured scattering and a temporal encoder for constraining over-scattering through mean squared error minimization between neighboring time steps. We introduce a contrastive fusion mechanism to ensure the complementarity of the learned temporal and topological representations. Additionally, we theoretically show that maximizing the conditional mutual information between temporal and topological views improves cross-view consistency and enhances more discriminative representations. Extensive experiments on multiple public benchmarks show that ScatterAD achieves state-of-the-art performance on multivariate time series anomaly detection.

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: ❶ Testing Adequacy : Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. ❷ Interpretability : The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which incorrect attributes or patterns of the model are triggered by the test inputs. This lack of interpretability hampers the subsequent debugging and fixing process. Therefore, there is an urgent need for a novel fuzzing criterion that offers improved testing adequacy, better interpretability, and more effective failure detection capabilities for DNNs. To alleviate these limitations, we propose NSGen, an approach for DNN fuzzing that utilizes neuron semantics as guidance during test generation. NSGen identifies critical neurons, translates their high-level semantic features into natural language descriptions, and then assembles them into human-readable DNN decision paths (representing the internal decision of the DNN). With these decision paths, we can generate more fault-revealing test inputs by quantifying the similarity between original test inputs and mutated test inputs for fuzzing. We evaluate NSGen on popular DNN models (VGG16_BN, ResNet50, and MobileNet_v2) using CIFAR10, CIFAR100, Oxford 102 Flower, and ImageNet datasets. Compared to 12 existing coverage-guided fuzzing criteria, NSGen outperforms all baselines, increasing the number of triggered faults by 21.4% to 61.2% compared to the state-of-the-art coverage-guided fuzzing criterion. This demonstrates NSGen's effectiveness in generating fault-revealing test inputs through guided input mutation, highlighting its potential to enhance DNN testing and interpretability.

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable.In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonly used 3 RNN classification models, and compare it with NC-based, uncertainty-based, and other black-box methods. Experimental results demonstrate that DeepVec achieves an average relative improvement of 12.5%-118.22% over baseline methods in selecting fault-revealing test cases with time costs reduced to only 1% to 1‱. At the same time, we find that the absolute accuracy improvement after retraining outperforms baseline methods by 0.29%-24.01% when selecting 15% data to retrain.