AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability
Fuente:
arXiv
Saved in:
| Main Author: | Bandarupalli, Gopichand |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java
by: Bandarupalli, Gopichand
Published: (2025)
by: Bandarupalli, Gopichand
Published: (2025)
The Impact of Software Testing with Quantum Optimization Meets Machine Learning
by: Bandarupalli, Gopichand
Published: (2025)
by: Bandarupalli, Gopichand
Published: (2025)
CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
by: Thillen, Alex, et al.
Published: (2026)
by: Thillen, Alex, et al.
Published: (2026)
Software Vulnerability Detection Using a Lightweight Graph Neural Network
by: Farmer, Miles, et al.
Published: (2026)
by: Farmer, Miles, et al.
Published: (2026)
Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
by: Bhuiyan, Masudul Hasan Masud, et al.
Published: (2025)
by: Bhuiyan, Masudul Hasan Masud, et al.
Published: (2025)
Enhancing Software Vulnerability Detection Using Code Property Graphs and Convolutional Neural Networks
by: Saimbhi, Amanpreet Singh
Published: (2025)
by: Saimbhi, Amanpreet Singh
Published: (2025)
Predicting Open Source Software Sustainability with Deep Temporal Neural Hierarchical Architectures and Explainable AI
by: Karim, S M Rakib Ul, et al.
Published: (2026)
by: Karim, S M Rakib Ul, et al.
Published: (2026)
LLMs in Coding and their Impact on the Commercial Software Engineering Landscape
by: Belozerov, Vladislav, et al.
Published: (2025)
by: Belozerov, Vladislav, et al.
Published: (2025)
Teaching Code Refactoring Using LLMs
by: Khairnar, Anshul, et al.
Published: (2025)
by: Khairnar, Anshul, et al.
Published: (2025)
Graph Neural Networks based Log Anomaly Detection and Explanation
by: Li, Zhong, et al.
Published: (2023)
by: Li, Zhong, et al.
Published: (2023)
AI-Assisted Unit Test Writing and Test-Driven Code Refactoring: A Case Study
by: Smolic, Ema, et al.
Published: (2026)
by: Smolic, Ema, et al.
Published: (2026)
Challenges and Paths Towards AI for Software Engineering
by: Gu, Alex, et al.
Published: (2025)
by: Gu, Alex, et al.
Published: (2025)
Predicting the First Response Latency of Maintainers and Contributors in Pull Requests
by: Khatoonabadi, SayedHassan, et al.
Published: (2023)
by: Khatoonabadi, SayedHassan, et al.
Published: (2023)
Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification
by: Yang, Guang, et al.
Published: (2023)
by: Yang, Guang, et al.
Published: (2023)
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
by: Wang, Juntong, et al.
Published: (2026)
by: Wang, Juntong, et al.
Published: (2026)
Optimizing AI-Assisted Code Generation
by: Torka, Simon, et al.
Published: (2024)
by: Torka, Simon, et al.
Published: (2024)
Protocode: Prototype-Driven Interpretability for Code Generation in LLMs
by: Bodla, Krishna Vamshi, et al.
Published: (2025)
by: Bodla, Krishna Vamshi, et al.
Published: (2025)
A Theoretical Analysis of Test-Driven Code Generation
by: Menet, Nicolas, et al.
Published: (2026)
by: Menet, Nicolas, et al.
Published: (2026)
Protocol-Driven Development: Governing Generated Software Through Invariants and Continuous Evidence
by: He, Jun, et al.
Published: (2026)
by: He, Jun, et al.
Published: (2026)
Functional Overlap Reranking for Neural Code Generation
by: To, Hung Quoc, et al.
Published: (2023)
by: To, Hung Quoc, et al.
Published: (2023)
The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
by: Li, Hao, et al.
Published: (2025)
by: Li, Hao, et al.
Published: (2025)
Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs
by: Yang, Dayu, et al.
Published: (2025)
by: Yang, Dayu, et al.
Published: (2025)
VibeTensor: System Software for Deep Learning, Fully Generated by AI Agents
by: Xu, Bing, et al.
Published: (2026)
by: Xu, Bing, et al.
Published: (2026)
Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents
by: Yang, Zhenning, et al.
Published: (2025)
by: Yang, Zhenning, et al.
Published: (2025)
Advancing Software Security and Reliability in Cloud Platforms through AI-based Anomaly Detection
by: Saleh, Sabbir M., et al.
Published: (2024)
by: Saleh, Sabbir M., et al.
Published: (2024)
Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability
by: Borg, Markus, et al.
Published: (2025)
by: Borg, Markus, et al.
Published: (2025)
StepShield: When, Not Whether to Intervene on Rogue Agents
by: Felicia, Gloria, et al.
Published: (2026)
by: Felicia, Gloria, et al.
Published: (2026)
Enhancing LLM-Based Test Generation by Eliminating Covered Code
by: Xu, WeiZhe, et al.
Published: (2026)
by: Xu, WeiZhe, et al.
Published: (2026)
Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
by: Jacopin, Éric
Published: (2026)
by: Jacopin, Éric
Published: (2026)
asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit
by: Koch, Norman, et al.
Published: (2025)
by: Koch, Norman, et al.
Published: (2025)
RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code
by: Gautam, Dhruv, et al.
Published: (2025)
by: Gautam, Dhruv, et al.
Published: (2025)
A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
by: Raheem, Mira, et al.
Published: (2025)
by: Raheem, Mira, et al.
Published: (2025)
BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice
by: Li, Yuanpeng, et al.
Published: (2025)
by: Li, Yuanpeng, et al.
Published: (2025)
NeuFair: Neural Network Fairness Repair with Dropout
by: Dasu, Vishnu Asutosh, et al.
Published: (2024)
by: Dasu, Vishnu Asutosh, et al.
Published: (2024)
Monitizer: Automating Design and Evaluation of Neural Network Monitors
by: Azeem, Muqsit, et al.
Published: (2024)
by: Azeem, Muqsit, et al.
Published: (2024)
RBT4DNN: Requirements-based Testing of Neural Networks
by: Mozumder, Nusrat Jahan, et al.
Published: (2025)
by: Mozumder, Nusrat Jahan, et al.
Published: (2025)
SoundnessBench: A Soundness Benchmark for Neural Network Verifiers
by: Zhou, Xingjian, et al.
Published: (2024)
by: Zhou, Xingjian, et al.
Published: (2024)
Analysing the Behaviour of Tree-Based Neural Networks in Regression Tasks
by: Samoaa, Peter, et al.
Published: (2024)
by: Samoaa, Peter, et al.
Published: (2024)
Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
by: Elboher, Yizhak Y., et al.
Published: (2026)
by: Elboher, Yizhak Y., et al.
Published: (2026)
Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations
by: Latendresse, Jasmine, et al.
Published: (2024)
by: Latendresse, Jasmine, et al.
Published: (2024)
Similar Items
-
Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java
by: Bandarupalli, Gopichand
Published: (2025) -
The Impact of Software Testing with Quantum Optimization Meets Machine Learning
by: Bandarupalli, Gopichand
Published: (2025) -
CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
by: Thillen, Alex, et al.
Published: (2026) -
Software Vulnerability Detection Using a Lightweight Graph Neural Network
by: Farmer, Miles, et al.
Published: (2026) -
Call Me Maybe: Enhancing JavaScript Call Graph Construction using Graph Neural Networks
by: Bhuiyan, Masudul Hasan Masud, et al.
Published: (2025)