arXiv AI

Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System

arXiv:2512. 02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or language translation.

arXiv AI

2d ago

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

arXiv:2608. 13681v1 Announce Type: cross Abstract: Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving the functional behavior of legacy systems.

By Pu Zhao, Changdi Yang, Yixiao Chen, Yi Gao, Yifan Cao, Haochen Zeng, Yanzhi Wang

llms agents fine-tuning safety

arXiv AI

Jul 16

Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code

arXiv:2607. 13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult.

By Niels M\"undler-Sasahara, Hristo Venev, Dawn Song, Martin Vechev, Jingxuan He

llms

arXiv AI

Jun 17

Unlocking LLM Code Correction with Iterative Feedback Loops

arXiv:2606. 17514v1 Announce Type: cross Abstract: Large Language Models have shown remarkable capabilities in code generation.

By Le Zhang, Suresh Kothari

llms

arXiv AI

Jul 7

Effectiveness of LLM-based Software Diversity for Reliability Improvement -- an Empirical Study

arXiv:2607. 03174v1 Announce Type: cross Abstract: Software diversity has been extensively studied as a means of reducing the risk of common-mode failures.

By Gabriel Almeida, Ilir Gashi, Vladimir Stankovic, Jo\~ao R. Campos

llms

arXiv AI

Jul 23

Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes

arXiv:2607. 19843v1 Announce Type: cross Abstract: Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained.

By Yuhao Tan, Zhibang Yang, Fangkai Yang, Yuan Yao, Yu Kang, Lu Wang, Pu Zhao, Xin Zhang, Xiaoxing Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

llms benchmarks

arXiv AI

Aug 6

ExeCRE: Execution-Consistency Guided Reliability Estimation for Self-Correcting Code Generation

arXiv:2608. 04439v1 Announce Type: cross Abstract: Large language models (LLMs) have made notable progress in code generation, but they still struggle on challenging tasks that require sophisticated algorithms or complex implementations.

By Yiru Dong, Richong Zhang, Fanshuang Kong, Si Chen

llms

arXiv Machine Learning

Jul 1

An Empirical Study of Security Calibration in Large Language Models for Code

arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?

By Mohammed Latif Siddiq, Md. Nafiu Rahman, Joanna C. S. Santos

llms benchmarks

arXiv AI

1d ago

Can LLMs Reason Like Automated Theorem Provers for Rust Verification? VCoT-Bench: Evaluating via Verification Chain of Thought

arXiv:2603. 18334v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly assist secure software development, their ability to meet the rigorous demands of Rust program verification remains unclear.

By Zichen Xie, Wenxi Wang

llms benchmarks

arXiv AI

1d ago

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair

arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).

By David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms benchmarks

arXiv AI

Jul 7

Evaluating LLM-Based Regression Test Generation

arXiv:2501. 11086v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown tremendous promise in automated software engineering.

By Jing Liu, Seongmin Lee, Eleonora Losiouk, Marcel B\"ohme

llms nlp benchmarks

arXiv AI

Jun 6

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.

By Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

llms fine-tuning

arXiv AI

Jun 2

Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks

arXiv:2606. 00920v1 Announce Type: cross Abstract: Run-level pass rate overstates retry-free coverage by up to 17.

By Yongxi Zhou, Lai Yun Choi, Jiaxi Wen, Wenbo Ye

llms benchmarks

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