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.
FLARE: Fine-Grained Diagnostic Feedback for LLM Code Refinement
arXiv:2606. 03852v1 Announce Type: cross Abstract: Large language models often generate code with bugs.
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arXiv AI
Jun 2
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults
arXiv:2505. 19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems.
arXiv AI
Jul 23
PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization
arXiv:2607. 19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases.
arXiv AI
Jul 28
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
arXiv:2607. 22883v1 Announce Type: cross Abstract: While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code.
arXiv Machine Learning
Jun 10
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.
Hugging Face Trending Papers
Jul 24
Evaluating and Mitigating the Misguidance Effect of Buggy Code in LLM-Generated Unit Tests
While Large Language Models (LLMs) show great promise for automating unit test generation, recent studies suggest that the quality of generated tests can be negatively impacted when models are prompted with buggy code. This paper presents a new metric to quantitatively measure the "misguidance effect," a phenomenon where buggy code steers LLMs toward generating tests that validate its erroneous behavior rather than expose it.
arXiv AI
Jun 24
VeriPilot: An LLM-Powered Verilog Debugging Framework
arXiv:2606. 23759v1 Announce Type: cross Abstract: Verilog debugging remains one of the most time-consuming stages in digital circuit design.
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
6d ago
VICBench: A Multi-Language Benchmark for Code Vulnerability Detection
arXiv:2608. 12246v1 Announce Type: cross Abstract: Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases.
arXiv AI
Aug 11
Contamination Means Overestimation? A Fine-Grained Empirical Study in Code Intelligence
arXiv:2506. 02791v4 Announce Type: replace-cross Abstract: In recent years, code intelligence has gained increasing importance in the field of automated software engineering.
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.
Hugging Face Trending Papers
Aug 12
VICBench: A Multi-Language Benchmark for Code Vulnerability Detection
Evaluating security vulnerability detection tools requires benchmark datasets with vulnerability-inducing commits (VICs) - the commits that first introduce vulnerabilities into codebases. VICs are essential for determining the full range of vulnerable software versions.