ReMCTS is a Monte Carlo Tree Search–style framework that enhances code generation by large language models (LLMs). It organizes program candidates as tree states, retains branch‑local debugging context, and retrieves failure experience across branches, distinguishing failed checks from unavailable evidence. Experiments on HumanEval and MBPP‑Sanitized show that visible‑test ReMCTS improves performance over direct generation in 8 of 10 model‑dataset pairs, while a pilot on HumanEval‑X C++ demonstrates compatibility with compiler‑backed execution.
arXiv:2609.06229v1 Announce Type: cross Abstract: Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical...
arXiv:2610.15894v1 Announce Type: cross Abstract: Large language model (LLM) code-repair systems fix faulty programs by repeatedly generating and testing revisions. Each revision is costly, making th...
arXiv:2507. 11059v3 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) in software engineering has revealed critical limitations in existing benchmarks, particularly the widely used SWE-bench dataset.
The paper introduces MCR-Bench, a benchmark for realistic multi‑round code review that includes 2,269 real‑world tasks across five programming languages, each annotated with fine‑grained defect information and dynamic state labels. Experiments with mainstream large language models show limited overall performance, especially as interaction rounds increase, and reveal that model accuracy varies by defect type and severity. Error analysis identifies key failure mechanisms such as cross‑round temporal misalignment and insufficient long‑range memory.
The paper introduces SWE-Flux, a repository‑level benchmark designed to test large language models’ ability to reason about runtime behavior. It contains 480 execution‑grounded instances from 12 real Python repositories, with gold answers automatically harvested from instrumented test executions. Evaluation of five LLMs shows the task remains difficult, with the best model achieving only 37% accuracy, and the benchmark can generate challenging variants through input perturbation.
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).
arXiv:2607. 18260v1 Announce Type: new Abstract: We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis.
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.
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.
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.
arXiv:2606. 03852v1 Announce Type: cross Abstract: Large language models often generate code with bugs.
The paper introduces BUSTA, a method for training small language models to reason about binary execution. By fine‑tuning Qwen3‑1.7B and Nemotron‑3‑Nano with Group Relative Policy Optimization, the authors improve the models’ ability to predict input register values for a given x86‑64 basic block, raising accuracy from 73.2% to 85.3% and from 63.3% to 95.2% respectively. The fine‑tuned Nemotron‑3‑Nano achieves performance within four points of frontier models while using far fewer parameters, and the authors release both the fine‑tuned models and the training dataset.