Saved in:
| Main Authors: | Nguyen, Thanh-Dat, Zhou, Yang, Le, Xuan Bach D., Thongtanunam, Patanamon, Lo, David |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2308.11161 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Systematic Survey on Debugging Techniques for Machine Learning Systems
by: Nguyen, Thanh-Dat, et al.
Published: (2025)
by: Nguyen, Thanh-Dat, et al.
Published: (2025)
Encoding Version History Context for Better Code Representation
by: Nguyen, Huy, et al.
Published: (2024)
by: Nguyen, Huy, et al.
Published: (2024)
Code Ownership: The Principles, Differences, and Their Associations with Software Quality
by: Thongtanunam, Patanamon, et al.
Published: (2024)
by: Thongtanunam, Patanamon, et al.
Published: (2024)
Enhancing Neural Code Representation with Additional Context
by: Nguyen, Huy, et al.
Published: (2025)
by: Nguyen, Huy, et al.
Published: (2025)
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
by: Liu, Chunhua, et al.
Published: (2025)
by: Liu, Chunhua, et al.
Published: (2025)
Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses
by: Charoenwet, Wachiraphan, et al.
Published: (2023)
by: Charoenwet, Wachiraphan, et al.
Published: (2023)
Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
by: Liu, Chunhua, et al.
Published: (2025)
by: Liu, Chunhua, et al.
Published: (2025)
Improving Automated Code Reviews: Learning from Experience
by: Lin, Hong Yi, et al.
Published: (2024)
by: Lin, Hong Yi, et al.
Published: (2024)
Enhancing Code Review through Fuzzing and Likely Invariants
by: Charoenwet, Wachiraphan, et al.
Published: (2025)
by: Charoenwet, Wachiraphan, et al.
Published: (2025)
A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile
by: Pasuksmit, Jirat, et al.
Published: (2024)
by: Pasuksmit, Jirat, et al.
Published: (2024)
An Empirical Study of Static Analysis Tools for Secure Code Review
by: Charoenwet, Wachiraphan, et al.
Published: (2024)
by: Charoenwet, Wachiraphan, et al.
Published: (2024)
Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
by: Khant, Kyi Shin, et al.
Published: (2025)
by: Khant, Kyi Shin, et al.
Published: (2025)
Automatically Recommend Code Updates: Are We There Yet?
by: Liu, Yue, et al.
Published: (2022)
by: Liu, Yue, et al.
Published: (2022)
Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification
by: Nguyen, Linh, et al.
Published: (2025)
by: Nguyen, Linh, et al.
Published: (2025)
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
by: Lin, Hong Yi, et al.
Published: (2025)
by: Lin, Hong Yi, et al.
Published: (2025)
Inferring Properties of Graph Neural Networks
by: Nguyen, Dat, et al.
Published: (2024)
by: Nguyen, Dat, et al.
Published: (2024)
Context-Augmented Code Generation Using Programming Knowledge Graphs
by: Seddik, Shahd, et al.
Published: (2026)
by: Seddik, Shahd, et al.
Published: (2026)
AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
by: Oliveira, Eduardo, et al.
Published: (2026)
by: Oliveira, Eduardo, et al.
Published: (2026)
When AI Models Become Dependencies: Studying the Evolution of Pre-Trained Model Reuse in Downstream Software Systems
by: Banyongrakkul, Peerachai, et al.
Published: (2026)
by: Banyongrakkul, Peerachai, et al.
Published: (2026)
From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers
by: Banyongrakkul, Peerachai, et al.
Published: (2025)
by: Banyongrakkul, Peerachai, et al.
Published: (2025)
Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing
by: Le-Cong, Thanh, et al.
Published: (2024)
by: Le-Cong, Thanh, et al.
Published: (2024)
Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
by: Lin, Hong Yi, et al.
Published: (2026)
by: Lin, Hong Yi, et al.
Published: (2026)
HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation
by: Tantithamthavorn, Kla, et al.
Published: (2026)
by: Tantithamthavorn, Kla, 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)
Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
by: Li, Shuochuan, et al.
Published: (2025)
by: Li, Shuochuan, et al.
Published: (2025)
Hotfixing Large Language Models for Code
by: Yang, Zhou, et al.
Published: (2024)
by: Yang, Zhou, et al.
Published: (2024)
Automatic Programming: Large Language Models and Beyond
by: Lyu, Michael R., et al.
Published: (2024)
by: Lyu, Michael R., et al.
Published: (2024)
Breaking Changes in Software Ecosystems: A Systematic Literature Review
by: Chen, Juntao, et al.
Published: (2026)
by: Chen, Juntao, et al.
Published: (2026)
AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
by: Charoenwet, Wachiraphan, et al.
Published: (2026)
by: Charoenwet, Wachiraphan, et al.
Published: (2026)
Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian
by: Hong, Yang, et al.
Published: (2024)
by: Hong, Yang, et al.
Published: (2024)
Signature in Code Backdoor Detection, how far are we?
by: Le, Quoc Hung, et al.
Published: (2025)
by: Le, Quoc Hung, et al.
Published: (2025)
PatchZero: Zero-Shot Automatic Patch Correctness Assessment
by: Zhou, Xin, et al.
Published: (2023)
by: Zhou, Xin, et al.
Published: (2023)
Ecosystem of Large Language Models for Code
by: Yang, Zhou, et al.
Published: (2024)
by: Yang, Zhou, et al.
Published: (2024)
CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases
by: Hoang, Anh Nguyen, et al.
Published: (2025)
by: Hoang, Anh Nguyen, et al.
Published: (2025)
What Types of Code Review Comments Do Developers Most Frequently Resolve?
by: Goldman, Saul, et al.
Published: (2025)
by: Goldman, Saul, et al.
Published: (2025)
Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation
by: Le-Cong, Thanh, et al.
Published: (2024)
by: Le-Cong, Thanh, et al.
Published: (2024)
Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
by: Liu, Yang, et al.
Published: (2025)
by: Liu, Yang, et al.
Published: (2025)
Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection
by: Zhou, Xin, et al.
Published: (2024)
by: Zhou, Xin, et al.
Published: (2024)
Do Not Treat Code as Natural Language: Implications for Repository-Level Code Generation and Beyond
by: Le-Anh, Minh, et al.
Published: (2026)
by: Le-Anh, Minh, et al.
Published: (2026)
Verifying DNN-based Semantic Communication Against Generative Adversarial Noise
by: Le, Thanh, et al.
Published: (2026)
by: Le, Thanh, et al.
Published: (2026)
Similar Items
-
A Systematic Survey on Debugging Techniques for Machine Learning Systems
by: Nguyen, Thanh-Dat, et al.
Published: (2025) -
Encoding Version History Context for Better Code Representation
by: Nguyen, Huy, et al.
Published: (2024) -
Code Ownership: The Principles, Differences, and Their Associations with Software Quality
by: Thongtanunam, Patanamon, et al.
Published: (2024) -
Enhancing Neural Code Representation with Additional Context
by: Nguyen, Huy, et al.
Published: (2025) -
Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
by: Liu, Chunhua, et al.
Published: (2025)