arXiv AI
Jun 30
Diff-Based Code Corruption using LLMs for Large-Scale Bugfix Benchmarking
arXiv:2606. 29088v1 Announce Type: cross Abstract: There are various benchmarks to evaluate bugfixing capabilities of Large Language Models.
Multi-task LLMs for Bug Classification: Efficient Inference with Auxiliary Decoding Heads
arXiv:2606. 09956v1 Announce Type: cross Abstract: The rapid adoption of LLM-powered code generation has dramatically accelerated software development, yet effective verification methods remain severely underdeveloped.
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arXiv AI
Jul 3
BLAgent: Agentic RAG for File-Level Bug Localization
arXiv:2605. 17965v2 Announce Type: replace-cross Abstract: Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program repair (APR).
arXiv AI
Jun 3
FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement
arXiv:2606. 03852v1 Announce Type: cross Abstract: Large language models often generate code with bugs.
arXiv AI
Jun 24
JupOtter: Cell-Level Bug Detection in Jupyter Notebooks
arXiv:2606. 23877v1 Announce Type: cross Abstract: Jupyter Notebooks are an increasingly popular coding environment used across many domains, especially in Python-based data science and scientific computing.
arXiv AI
Aug 3
RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.
arXiv AI
2d ago
Rethinking Automated Program Repair: The Impact of Bug Complexity, Fault Localization, and LLM Cost-efficiency
arXiv:2608. 14065v1 Announce Type: cross Abstract: Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques.
arXiv AI
Jul 9
AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection
arXiv:2604. 11950v2 Announce Type: replace-cross Abstract: While recent LLM-based agents can identify many candidate bugs in source code, their reports remain static hypotheses that require manual validation, limiting the practicality of automated bug detection.
arXiv AI
Jul 2
SWE-Doctor: Guiding Software Engineering Agents with Runtime Diagnosis from Multi-Faceted Bug Reproduction Tests
arXiv:2607. 00990v1 Announce Type: cross Abstract: Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories.
arXiv AI
Jun 16
FasterPy: An LLM-based Code Execution Efficiency Optimization Framework
arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.
arXiv AI
Jun 2
Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference
arXiv:2606. 02430v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decision-making.
arXiv AI
6d ago
Program Semantic Inequivalence Game with Large Language Models
arXiv:2505. 03818v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.
arXiv AI
Jul 29
How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair
arXiv:2607. 25873v1 Announce Type: cross Abstract: Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent.