arXiv subjects

Jia Feng

Publications and source records attributed to Jia Feng.

11 recordsLinked to original sources

arXiv2026

Memoir: Learning, Verifying, and Evolving False-Positive Memories for Static Application Security Testing Tools

Static Application Security Testing (SAST) tools have become indispensable in modern secure software devel- opment. However, these tools often generate false-positive (FP) alerts, imposing substantial manual inspection costs and reducing the trust from developers. Existing FP reduction methods still face two primary challenges. First, the large differences among SAST tools and vulnerability categories make it difficult for these methods to learn recurring patterns in historical false positives. Moreover, the knowledge used by these methods are largely static and cannot be updated as newly validated cases accumulate. To address these challenges, we propose Memoir, a memory- driven framework for identifying false positives by transform- ing historical FP alerts into reusable semantic memories. It consists of two key modules. First, historical semantic memory construction converts historical FP alerts into structured semantic memories through LLM-guided annotation, pattern clustering, and memory synthesis to capture reusable behavioral patterns. Moreover, memory-driven identification and evolution retrieves relevant memories and performs semantic verification against taxonomy consistency and security invariants before making the final prediction. It then incorporates verified predictions back into the memory repository, allowing the knowledge base to evolve as new cases accumulate. We evaluate Memoir on CWE- Bench-Java to demonstrate its effectiveness in real-world security analysis. Specifically, Memoir achieves an F1-score of 99.43% with a Recall of 98.88% and perfect Precision, consistently outperforming other baselines. Furthermore, an industrial case study on production software systems from a top IT company shows that the learned memory base generalizes effectively across different SAST tools without retraining.

cs.SE↗

arXiv2026

HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer. Although artificial intelligence methods have advanced flood prediction and model-error correction, most existing studies have not explicitly represented the tacit expert rules, review checkpoints, and workflow constraints that connect model outputs to operational warning decisions. To address this issue, we propose HydroAgent, a skill-orchestrated agent framework that embeds Large Language Models (LLMs) into a model-driven flood forecasting workflow, where each skill encodes explicit rules to bound LLM reasoning. We validated its effectiveness using five state-of-the-art LLMs in the South Yamhill River basin. Our results demonstrate that prior judgment captures observed peak flow and flood volume within 5% tolerance in 10 and 11 out of 14 events, with 5-fold cross-validation over 129 events yielding Pearson correlations of 0.62 and 0.84. Building on a high-baseline scheme library (average KGE 0.890), the guided scheme selection further improves KGE by 0.023-0.154, with simulated peak flow and flood volume falling within the prior judgment ranges for 14 and 13 out of 14 events. All five tested LLMs successfully execute the HydroAgent workflow with comparable judgment accuracy (40%-80%), while showing moderate performance variation and substantial cost differences. HydroAgent does not aim to replace human forecasters; instead, it translates their tacit expertise into an auditable and reproducible workflow, streamlining analytical steps and supporting more informed decision-making. This skill-orchestrated paradigm demonstrates how explicit rule boundaries can guide language model reasoning to complement physically based simulation in next-generation flood forecasting.

physics.geo-ph↗

arXiv2026

Towards Reliable C-to-Rust Translation with Rule-Guided Reasoning and Reinforcement Learning

The migration of legacy C programs to Rust has become an important direction for improving software memory safety while alleviating the high cost of manual rewriting. Leveraging large language models (LLMs) for automated C-to-Rust translation has emerged as a promising direction. However, existing LLM-based approaches remain limited. On the one hand, LLMs exhibit limited capability in identifying Rust-specific rules, and inadequate handling of Rust syntax often results in incorrect translations. On the other hand, existing LLMs often struggle to accurately capture the semantics of complex code, resulting in incorrect translations. To address these challenges, we propose a Translation fRAmework Via rule-guided reasoning and rEinforcement Learning, namely TRAVEL, consisting of two modules. The first module employs Monte Carlo Tree Search (MCTS)-based reasoning path construction guided by Rust-specific rules, steering the search toward translation steps that respect the syntactic rules that LLMs frequently violate. The second module introduces reinforcement learning that couples execution feedback with reasoning-quality signals, encouraging the model to construct reasoning paths that accurately capture program semantics, thereby ensuring that the generated Rust code preserves the intended behavior of the original C program. We evaluate TRAVEL on three datasets: xCodeEval (a public benchmark), OS-Bench (functions collected from the Linux kernel), and HW-Bench (an industrial dataset from Huawei). On xCodeEval, TRAVEL outperforms all baselines across three backbone LLMs. In particular, compared to the strongest prompting baseline IRENE, TRAVEL improves computational accuracy (CA) by 26.22% and compilation success rate (CSR) by 18.77%. On HW-Bench and OS-Bench, TRAVEL further improves CSR by 18.28% and 16.51%, respectively, while reducing unsafe rate (UR) by 13.06% and 13.08%, respectively.

cs.SE↗

arXiv2026

Enhancing the Code Reasoning Capabilities of LLMs via Consistency-based Reinforcement Learning

Code reasoning refers to the task of predicting the output of a program given its source code and specific inputs. It can measure the reasoning capability of large language models (LLMs) and also benefit downstream tasks such as code generation and mathematical reasoning. Existing work has verified the effectiveness of reinforcement learning on the task. However, these methods design rewards solely based on final outputs or coarse-grained signals, and neglect the inherent consistency of the stepwise reasoning process in the task. Therefore, these methods often result in sparse reward or reward hacking, which limits the full play of enhanced learning capabilities. To alleviate these issues, we propose CodeThinker, a consistency-driven reinforcement learning framework for code reasoning. Specifically, CodeThinker has three key components: (1) a stepwise reasoning-aware model training module, which utilizes a consistency tracing paradigm as a template to synthesize training data that captures the stepwise reasoning process; (2) a dynamic beam sampling strategy, which aims to improve the quality of sampled outputs under a fixed sampling budget; and (3) a consistency reward mechanism that can effectively alleviate reward hacking. Experiments on three popular benchmarks show that CodeThinker achieves state-of-the-art performance across multiple LLMs. For instance, it outperforms the strongest baseline by 4.3% in accuracy when deployed on Qwen2.5-Coder-7B-Instruct. We also validate the effectiveness of CodeThinker on downstream tasks. Results show that, without additional training, CodeThinker obtains average accuracy gains of 5.33 and 3.11 percentage points on mathematical reasoning and code reasoning tasks covering 17 programming languages, respectively.

cs.LG↗

arXiv2026

On the Effectiveness of Context Compression for Repository-Level Tasks: An Empirical Investigation

Repository-level code intelligence tasks require large language models (LLMs) to process long, multi-file contexts. Such inputs introduce three challenges: crucial context can be obscured by noise, truncated due to limited windows, and increased inference latency. Context compression mitigates these risks by condensing inputs. While studied in NLP, its applicability to code tasks remains largely unexplored. We present the first systematic empirical study of context compression for repository-level code intelligence, organizing eight methods into three paradigms: discrete token sequences, continuous latent vectors, and visual tokens. We evaluate them on code completion and generation, measuring performance and efficiency. Results show context compression is effective: at 4x compression, continuous latent vector methods surpass full-context performance by up to 28.3% in BLEU score, indicating they filter noise rather than just truncating. On efficiency, all paradigms reduce inference cost. Both visual and text-based compression achieve up to 50% reduction in end-to-end latency at high ratios, approaching the cost of inference without repository context. These findings establish context compression as a viable approach and provide guidance for paradigm selection.

cs.SE↗

arXiv2026

Dependency-Guided Repository-Level C-to-Rust Translation with Reinforcement Alignment

Automating C-to-Rust migration is critical for improving software security without sacrificing performance. Traditional rule-based methods struggle with diverse C idioms, often producing rigid and unidiomatic Rust code. Large Language Models (LLMs), trained on massive code corpora, offer a promising alternative by leveraging cross-language generalization to generate more idiomatic and maintainable Rust code. However, several challenges remain. First, existing LLM-based approaches fail to handle cross-file dependencies effectively, either ignoring them or including entire files as context, which limits accurate dependency modeling. Second, complex dependencies and structured inputs and outputs make it difficult to verify syntactic correctness and functional equivalence at the repository level. Third, the lack of large-scale C-Rust parallel data constrains model performance. We propose DepTrans, a framework that combines model capability enhancement with structured inference. DepTrans introduces Reinforcement-Aligned Syntax Training to improve generation quality through multi-task fine-tuning and feedback-driven reinforcement learning. It further applies Dependency-Guided Iterative Refinement to capture fine-grained cross-file dependencies and iteratively refine generated Rust code. We construct a dataset of 85k training samples and a benchmark of 145 repository-level instances. Experiments show that DepTrans achieves a 60.7 percent compilation success rate and 43.5 percent computational accuracy, outperforming the strongest baseline by 22.8 and 17.3 percentage points. It also successfully builds 7 of 15 industrial C projects, demonstrating its practical potential.

cs.SE↗

arXiv2025

Integrating Rules and Semantics for LLM-Based C-to-Rust Translation

Automated translation of legacy C code into Rust aims to ensure memory safety while reducing the burden of manual migration. Early approaches in code translation rely on static rule-based methods, but they suffer from limited coverage due to dependence on predefined rule patterns. Recent works regard the task as a sequence-to-sequence problem by leveraging large language models (LLMs). Although these LLM-based methods are capable of reducing unsafe code blocks, the translated code often exhibits issues in following Rust rules and maintaining semantic consistency. On one hand, existing methods adopt a direct prompting strategy to translate the C code, which struggles to accommodate the syntactic rules between C and Rust. On the other hand, this strategy makes it difficult for LLMs to accurately capture the semantics of complex code. To address these challenges, we propose IRENE, an LLM-based framework that Integrates RulEs aNd sEmantics to enhance translation. IRENE consists of three modules: 1) a rule-augmented retrieval module that selects relevant translation examples based on rules generated from a static analyzer developed by us, thereby improving the handling of Rust rules; 2) a structured summarization module that produces a structured summary for guiding LLMs to enhance the semantic understanding of C code; 3) an error-driven translation module that leverages compiler diagnostics to iteratively refine translations. We evaluate IRENE on two datasets (xCodeEval, a public dataset, and HW-Bench, an industrial dataset provided by Huawei) and eight LLMs, focusing on translation accuracy and safety.

cs.SE↗

arXiv2024

ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code

In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limited scenarios, such as code generation or completion, which do not reflect the diverse challenges developers face in real-world contexts. To address this, we introduce ComplexCodeEval, a benchmark designed to assess LCMs in various development tasks, including code generation, completion, API recommendation, and test case generation. It includes 3,897 Java samples and 7,184 Python samples from high-star GitHub repositories, each annotated with function signatures, docstrings, and API references to simulate real development environments. Our experiments across ten LCMs reveal that context improves performance and that data leakage can lead to overestimation, highlighting the need for more accurate evaluations.

cs.SE↗

arXiv2024

Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability

Current serverless platforms struggle to optimize resource utilization due to their dynamic and fine-grained nature. Conventional techniques like overcommitment and autoscaling fall short, often sacrificing utilization for practicability or incurring performance trade-offs. Overcommitment requires predicting performance to prevent QoS violation, introducing trade-off between prediction accuracy and overheads. Autoscaling requires scaling instances in response to load fluctuations quickly to reduce resource wastage, but more frequent scaling also leads to more cold start overheads. This paper introduces Jiagu, which harmonizes efficiency with practicability through two novel techniques. First, pre-decision scheduling achieves accurate prediction while eliminating overheads by decoupling prediction and scheduling. Second, dual-staged scaling achieves frequent adjustment of instances with minimum overhead. We have implemented a prototype and evaluated it using real-world applications and traces from the public cloud platform. Our evaluation shows a 54.8% improvement in deployment density over commercial clouds (with Kubernetes) while maintaining QoS, and 81.0%--93.7% lower scheduling costs and a 57.4%--69.3% reduction in cold start latency compared to existing QoS-aware schedulers in research work.

cs.DC↗

arXiv2024

Dual-Tap Optical-Digital Feedforward Equalization Enabling High-Speed Optical Transmission in IM/DD Systems

Intensity-modulation and direct-detection (IM/DD) transmission is widely adopted for high-speed optical transmission scenarios due to its cost-effectiveness and simplicity. However, as the data rate increases, the fiber chromatic dispersion (CD) would induce a serious power fading effect, and direct detection could generate inter-symbol interference (ISI). Moreover, the ISI becomes more severe with the increase of fiber length, thereby highly restricting the transmission distance of IM/DD systems. This paper proposes a dual-tap optical-digital feedforward equalization (DT-ODFE) scheme, which could effectively compensate for CD-induced power fading while maintaining low cost and simplicity. A theoretical channel response is formulated for IM/DD transmission, incorporating a dual-tap optical equalizer, and the theoretical analysis reveals that for an IM/DD transmission using 1371nm over 10km standard single-mode fiber (SSMF), frequency notch is removed from 33.7GHz to 46GHz. Simulation results show that the DT- ODFE achieves an SNR gain of 2.3dB over IM/DD systems with symbol-space feedforward equalizer (FFE) alone. As the fiber length increases to 15 km, DT- ODFE performs well, while FFE, decision-feedback equalizer (DFE) and Volterra nonlinear equalizers (VNLE) all fail to compensate for the power fading and the 7% hard-decision FEC limit is not satisfied. For 200 Gb/s/$λ$ PAM-4 over 15km SSMF, results show that the signal-to-noise ratio (SNR) of the proposed DT- ODFE with optimal coefficients satisfies the 7% hard-decision FEC limit, which uncovers the great potential of the DT- ODFE for high-speed IM/DD systems in LR/FR scenarios.

eess.SP↗

arXiv2014

Effects of Crowding Perception on Self-organized Pedestrian Flows Using Adaptive Agent-based Model

Pedestrian behavior has much more complicated characteristics in a dense crowd and thus attracts the widespread interest of scientists and engineers. However, even successful modeling approaches such as pedestrian models based on particle systems are still not fully considered the perceptive mechanism underlying collective pedestrian behavior. This paper extends a behavioral heuristics-based pedestrian model to an adaptive agent-based model, which explicitly considers the crowding effect of neighboring individuals and perception anisotropy on the representation of a pedestrians visual information. The adaptive agents with crowding perception are constructed to investigate complex, selforganized collective dynamics of pedestrian motion. The proposed model simulates selforganized pedestrian flows in good quantitative agreement with empirical data. The selforganized phenomena include lane formation in bidirectional flow and fundamental diagrams of unidirectional flow. Simulation results show that the emergence of lane formation in bidirectional flow can be well reproduced. To investigate this further, increasing view distance has a significant effect on reducing the number of lanes, increasing lane width, and stabilizing the self-organized lanes. The paper also discusses phase transitions of fundamental diagrams of pedestrian crowds with unidirectional flow. It is found that the heterogeneity of how pedestrians perceive crowding in the population has a remarkable impact on the flow quality, which results in the buildup of congestion and rapidly decreases the efficiency of pedestrian flows. It also indicates that the concept of heterogeneity may be used to explain the instability of phase transitions.

cs.MA↗