arXiv subjects

Zike Li

Publications and source records attributed to Zike Li.

6 recordsLinked to original sources

arXiv2026

His2Trans: A Knowledge-Guided Agentic Framework for Project-Level C-to-Rust Migration

C remains a major implementation language for operating systems, embedded platforms, and infrastructure software, but manual memory management continues to create security and maintenance costs. Rust is a practical migration target because it retains low-level control while enforcing stronger memory-safety checks. At project scale, especially under gradual C/Rust coexistence, migration is not a sequence of syntax-preserving function rewrites. A translator must preserve project interfaces, observable behavior, system interaction protocols, and low-level interoperability boundaries while staying consistent with migration choices already made in the codebase. We introduce His2Trans, a knowledge-guided agentic framework for project-level C-to-Rust migration. His2Trans reuses interface-level and fragment-level knowledge mined from historical C/Rust migrations to guide new translations toward Rust interfaces, wrapper choices, and local idioms already accepted in the evolving project. It then refines the assembled crate with project-level agentic feedback. On ten OpenHarmony modules, His2Trans reaches a 100.00\% incremental compilation pass rate, a 94.92\% Test Pass Rate, and a 16.35\% Unsafe Ratio. On eight open-source C projects, it reaches 100.00\% for both incremental compilation and Test Pass Rate, reducing Unsafe Ratio from 42.88\% under C2Rust to 8.59\%. These results support knowledge-guided migration and project-level agentic refinement as practical mechanisms for preserving observable behavior while reducing the unsafe burden of rule-based transpilation.

cs.SE↗

arXiv2025

Knowledge Matters: Injecting Project and Testing Knowledge into LLM-based Unit Test Generation

Automated unit test generation using large language models (LLMs) holds great promise but often struggles with generating tests that are both correct and maintainable in real-world projects. This paper presents KTester, a novel framework that integrates project-specific knowledge and testing domain knowledge to enhance LLM-based test generation. Our approach first extracts project structure and usage knowledge through static analysis, which provides rich context for the model. It then employs a testing-domain-knowledge-guided separation of test case design and test method generation, combined with a multi-perspective prompting strategy that guides the LLM to consider diverse testing heuristics. The generated tests follow structured templates, improving clarity and maintainability. We evaluate KTester on multiple open-source projects, comparing it against state-of-the-art LLM-based baselines using automatic correctness and coverage metrics, as well as a human study assessing readability and maintainability. Results demonstrate that KTester significantly outperforms existing methods across six key metrics, improving execution pass rate by 5.69% and line coverage by 8.83% over the strongest baseline, while requiring less time and generating fewer test cases. Human evaluators also rate the tests produced by KTester significantly higher in terms of correctness, readability, and maintainability, confirming the practical advantages of our knowledge-driven framework.

cs.SE↗

arXiv2025

EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing

The rapid advancement of Large Language Models (LLMs) poses a significant challenge to existing mathematical reasoning benchmarks. However, these benchmarks tend to become easier over time as LLMs can learn from the published benchmarks. This limitation hinder the precise evaluation of the true capabilities of SOTA models. To address this challenge, this paper introduces EvolMathEval, an automated mathematical benchmark generation and evolution framework based on evolutionary testing. Experimental results demonstrate that EvolMathEval can not only generate a large volume of high-difficulty problems through continuous self-iteration, but it can also significantly enhance the complexity of public datasets like GSM8K through evolution, reducing model accuracy by an average of 48\%. Deeper investigation reveals that when solving these evolved problems, LLMs tend to bypass complex multi-step logical reasoning by relying on simplistic and fuzzy conditions, consequently leading to incorrect solutions. We define this phenomenon as the ``Pseudo Aha Moment", which we find accounts for 77\% to 100\% of errors on targeted problems. Code and resources are available at: https://anonymous.4open.science/r/EvolMathEval

cs.AI↗

arXiv2025

AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation

Code generation with large language models (LLMs) is highly sensitive to token selection during decoding, particularly at uncertain decision points that influence program logic. While standard strategies such as greedy decoding treat all tokens uniformly, they overlook code-specific uncertainty patterns, leading to suboptimal performance. This paper presents an empirical study revealing that many generation errors stem from token ranking mistakes at high-uncertainty steps, where the correct token is present but not top-ranked. Motivated by these findings, we propose AdaDec, a lookahead-based uncertainty-guided adaptive decoding framework that integrates a token-level pause-then-rerank mechanism driven by token uncertainty. AdaDec learns model-specific uncertainty thresholds and applies a lookahead-based reranking strategy when uncertainty is high. Experiments on HumanEval+, MBPP+, and DevEval benchmarks show that AdaDec improves Pass@1 accuracy by up to 20.9% in absolute terms over greedy decoding. More importantly, it consistently outperforms both competitive baselines like Beam Search and state-of-the-art adaptive decoding methods such as AdapT, while maintaining high efficiency through selective, uncertainty-triggered pausing. Our results highlight the promise of uncertainty-aware adaptive decoding for improving both the reliability and efficiency of LLM-based code generation.

cs.SE↗

arXiv2025

A Preliminary Study on the Robustness of Code Generation by Large Language Models

Robustness is a critical factor for reliable code generation by large language models, yet most evaluations focus on correctness and overlook key issues such as missing input validation and inadequate error handling. In this work, we present the first empirical study of LLM-generated code robustness using the CoderEval benchmark. Evaluating four state-of-the-art code LLMs, we find that 35.2% of their outputs are less robust than human-written code, with over 90% of deficiencies caused by missing conditional checks-70% of which occur in the first line. Interestingly, in 63% of cases where a conditional statement is needed but absent, the "if" token still ranks among the top three predictions, suggesting implicit recognition of control flow. To address these issues, we propose RobGen, a model-agnostic framework that improves robustness without retraining. RobGen combines a line-level intervention checker, which decides whether to adjust logits for each generated line, with token-level conditional logit adjustments to promote essential control structures. Experiments show that RobGen reduces the proportion of less robust code by 10%, achieves the highest average Pass@1 (43.57), and adds minimal overhead (+33.4%). As a lightweight and adaptable solution, RobGen effectively enhances the reliability of LLM-generated code across diverse tasks.

cs.SE↗

arXiv2024

A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning

Federated learning heavily relies on distributed gradient descent techniques. In the situation where gradient information is not available, the gradients need to be estimated from zeroth-order information, which typically involves computing finite-differences along isotropic random directions. This method suffers from high estimation errors, as the geometric features of the objective landscape may be overlooked during the isotropic sampling. In this work, we propose a non-isotropic sampling method to improve the gradient estimation procedure. Gradients in our method are estimated in a subspace spanned by historical trajectories of solutions, aiming to encourage the exploration of promising regions and hence improve the convergence. The proposed method uses a covariance matrix for sampling which is a convex combination of two parts. The first part is a thin projection matrix containing the basis of the subspace which is designed to improve the exploitation ability. The second part is the historical trajectories. We implement this method in zeroth-order federated settings, and show that the convergence rate aligns with existing ones while introducing no significant overheads in communication or local computation. The effectiveness of our proposal is verified on several numerical experiments in comparison to several commonly-used zeroth-order federated optimization algorithms.

cs.LG↗