Saved in:
| Main Authors: | Haque, Mirazul, Babkin, Petr, Farmahinifarahani, Farima, Veloso, Manuela |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.04441 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Teaching LLMs Program Semantics via Symbolic Execution Traces
by: Bayer, Jonas, et al.
Published: (2026)
by: Bayer, Jonas, et al.
Published: (2026)
Perturb Your Data: Paraphrase-Guided Training Data Watermarking
by: Shetty, Pranav, et al.
Published: (2025)
by: Shetty, Pranav, et al.
Published: (2025)
CigaR: Cost-efficient Program Repair with LLMs
by: Hidvégi, Dávid, et al.
Published: (2024)
by: Hidvégi, Dávid, et al.
Published: (2024)
Coherence Collapse: Diagnosing Why Code Agents Fail After Reaching the Right Code
by: Kim, Myeongsoo, et al.
Published: (2026)
by: Kim, Myeongsoo, et al.
Published: (2026)
Towards Verified Code Reasoning by LLMs
by: Sistla, Meghana, et al.
Published: (2025)
by: Sistla, Meghana, et al.
Published: (2025)
RepairBench: Leaderboard of Frontier Models for Program Repair
by: Silva, André, et al.
Published: (2024)
by: Silva, André, et al.
Published: (2024)
Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation
by: Diggs, Colin, et al.
Published: (2024)
by: Diggs, Colin, et al.
Published: (2024)
RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
by: Silva, André, et al.
Published: (2023)
by: Silva, André, et al.
Published: (2023)
A Semantic-based Optimization Approach for Repairing LLMs: Case Study on Code Generation
by: Gu, Jian, et al.
Published: (2025)
by: Gu, Jian, et al.
Published: (2025)
SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair
by: Zhang, Yifan, et al.
Published: (2026)
by: Zhang, Yifan, et al.
Published: (2026)
Renaissance of Literate Programming in the Era of LLMs: Enhancing LLM-Based Code Generation in Large-Scale Projects
by: Zhang, Wuyang, et al.
Published: (2024)
by: Zhang, Wuyang, et al.
Published: (2024)
Leveraging Code Cohesion Analysis to Identify Source Code Supply Chain Attacks
by: Reuben, Maor, et al.
Published: (2025)
by: Reuben, Maor, et al.
Published: (2025)
Whitespaces Don't Lie: Feature-Driven and Embedding-Based Approaches for Detecting Machine-Generated Code
by: Nirob, Syed Mehedi Hasan, et al.
Published: (2026)
by: Nirob, Syed Mehedi Hasan, et al.
Published: (2026)
Leveraging Reviewer Experience in Code Review Comment Generation
by: Lin, Hong Yi, et al.
Published: (2024)
by: Lin, Hong Yi, et al.
Published: (2024)
Automated Program Repair: Emerging trends pose and expose problems for benchmarks
by: Renzullo, Joseph, et al.
Published: (2024)
by: Renzullo, Joseph, et al.
Published: (2024)
Disproving Program Equivalence with LLMs
by: Allamanis, Miltiadis, et al.
Published: (2025)
by: Allamanis, Miltiadis, et al.
Published: (2025)
iML: Executable, Problem-Grounded, and Broadly Exploratory Code-Driven AutoML
by: Le, Dat, et al.
Published: (2026)
by: Le, Dat, et al.
Published: (2026)
Leveraging Reward Models for Guiding Code Review Comment Generation
by: Sghaier, Oussama Ben, et al.
Published: (2025)
by: Sghaier, Oussama Ben, et al.
Published: (2025)
Bootstrapping Coding Agents: The Specification Is the Program
by: Monperrus, Martin
Published: (2026)
by: Monperrus, Martin
Published: (2026)
Teaching Code Refactoring Using LLMs
by: Khairnar, Anshul, et al.
Published: (2025)
by: Khairnar, Anshul, et al.
Published: (2025)
Monte Carlo Tree Search for Execution-Guided Program Repair with Large Language Models
by: Liang, Yixuan
Published: (2026)
by: Liang, Yixuan
Published: (2026)
QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization
by: Ke, Changxin, et al.
Published: (2026)
by: Ke, Changxin, et al.
Published: (2026)
ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair
by: Lutellier, Thibaud, et al.
Published: (2019)
by: Lutellier, Thibaud, et al.
Published: (2019)
OSS-Bench: Benchmark Generator for Coding LLMs
by: Jiang, Yuancheng, et al.
Published: (2025)
by: Jiang, Yuancheng, et al.
Published: (2025)
Understanding Robustness of Model Editing in Code LLMs
by: Chhetri, Vinaik, et al.
Published: (2025)
by: Chhetri, Vinaik, et al.
Published: (2025)
MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
by: Ibrahimzada, Ali Reza, et al.
Published: (2025)
by: Ibrahimzada, Ali Reza, et al.
Published: (2025)
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
by: Gu, Alex, et al.
Published: (2024)
by: Gu, Alex, et al.
Published: (2024)
Aligning the Objective of LLM-based Program Repair
by: Xu, Junjielong, et al.
Published: (2024)
by: Xu, Junjielong, et al.
Published: (2024)
Gradient-Based Program Repair: Fixing Bugs in Continuous Program Spaces
by: Silva, André, et al.
Published: (2025)
by: Silva, André, et al.
Published: (2025)
How Robustly do LLMs Understand Execution Semantics?
by: Spiess, Claudio, et al.
Published: (2026)
by: Spiess, Claudio, et al.
Published: (2026)
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
by: Allamanis, Miltiadis, et al.
Published: (2024)
by: Allamanis, Miltiadis, et al.
Published: (2024)
Wisdom and Delusion of LLM Ensembles for Code Generation and Repair
by: Vallecillos-Ruiz, Fernando, et al.
Published: (2025)
by: Vallecillos-Ruiz, Fernando, et al.
Published: (2025)
Semantic Voting: Execution-Grounded Consensus for LLM Code Generation
by: Jiang, Shan, et al.
Published: (2026)
by: Jiang, Shan, et al.
Published: (2026)
Context-Augmented Code Generation Using Programming Knowledge Graphs
by: Seddik, Shahd, et al.
Published: (2026)
by: Seddik, Shahd, et al.
Published: (2026)
Mechanistic Interpretability of Code Correctness in LLMs via Sparse Autoencoders
by: Tahimic, Kriz, et al.
Published: (2025)
by: Tahimic, Kriz, et al.
Published: (2025)
Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
by: Palacio, David N., et al.
Published: (2024)
by: Palacio, David N., et al.
Published: (2024)
Execution-free Program Repair
by: Huang, Li, et al.
Published: (2024)
by: Huang, Li, et al.
Published: (2024)
Ensuring Reliability in Programming Knowledge Tracing: A Re-evaluation of Attention-augmented Models and Experimental Protocols
by: Kim, Jaewook, et al.
Published: (2026)
by: Kim, Jaewook, et al.
Published: (2026)
TritonRL: Training LLMs to Think and Code Triton Without Cheating
by: Woo, Jiin, et al.
Published: (2025)
by: Woo, Jiin, et al.
Published: (2025)
K-ASTRO: Structure-Aware Adaptation of LLMs for Code Vulnerability Detection
by: Zhang, Yifan, et al.
Published: (2022)
by: Zhang, Yifan, et al.
Published: (2022)
Similar Items
-
Teaching LLMs Program Semantics via Symbolic Execution Traces
by: Bayer, Jonas, et al.
Published: (2026) -
Perturb Your Data: Paraphrase-Guided Training Data Watermarking
by: Shetty, Pranav, et al.
Published: (2025) -
CigaR: Cost-efficient Program Repair with LLMs
by: Hidvégi, Dávid, et al.
Published: (2024) -
Coherence Collapse: Diagnosing Why Code Agents Fail After Reaching the Right Code
by: Kim, Myeongsoo, et al.
Published: (2026) -
Towards Verified Code Reasoning by LLMs
by: Sistla, Meghana, et al.
Published: (2025)