Copertina

Context-Augmented Code Generation Using Programming Knowledge Graphs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Seddik, Shahd, Seddik, Fahd, Saberi, Iman, Fard, Fatemeh, Huynh, Minh Hieu, Thongtanunam, Patanamon
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Software Engineering Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ARISE: A Repository-level Graph Representation and Toolset for Agentic Fault Localization and Program Repair
di: Seddik, Shahd, et al.
Pubblicazione: (2026)

Context-Augmented Code Generation Using Programming Knowledge Graphs
di: Saberi, Iman, et al.
Pubblicazione: (2024)

Analysis of AdvFusion: Adapter-based Multilingual Learning for Code Large Language Models
di: Esmaeili, Amirreza, et al.
Pubblicazione: (2025)

Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks
di: Khant, Kyi Shin, et al.
Pubblicazione: (2025)

Leveraging Reviewer Experience in Code Review Comment Generation
di: Lin, Hong Yi, et al.
Pubblicazione: (2024)

Encoding Version History Context for Better Code Representation
di: Nguyen, Huy, et al.
Pubblicazione: (2024)

Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R
di: Esmaeili, Amirreza, et al.
Pubblicazione: (2024)

Code Ownership: The Principles, Differences, and Their Associations with Software Quality
di: Thongtanunam, Patanamon, et al.
Pubblicazione: (2024)

A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
di: Joel, Sathvik, et al.
Pubblicazione: (2024)

Enhancing Neural Code Representation with Additional Context
di: Nguyen, Huy, et al.
Pubblicazione: (2025)

Automatic Programming: Large Language Models and Beyond
di: Lyu, Michael R., et al.
Pubblicazione: (2024)

Too Noisy To Learn: Enhancing Data Quality for Code Review Comment Generation
di: Liu, Chunhua, et al.
Pubblicazione: (2025)

Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian
di: Hong, Yang, et al.
Pubblicazione: (2024)

Standing on the Shoulders of Giants: Stabilized Knowledge Distillation for Cross--Language Code Clone Detection
di: Khajezade, Mohamad, et al.
Pubblicazione: (2026)

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
di: Liu, Chunhua, et al.
Pubblicazione: (2025)

AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection
di: Charoenwet, Wachiraphan, et al.
Pubblicazione: (2026)

AdvFusion: Adapter-based Knowledge Transfer for Code Summarization on Code Language Models
di: Saberi, Iman, et al.
Pubblicazione: (2023)

Utilization of Pre-trained Language Model for Adapter-based Knowledge Transfer in Software Engineering
di: Saberi, Iman, et al.
Pubblicazione: (2023)

Toward Effective Secure Code Reviews: An Empirical Study of Security-Related Coding Weaknesses
di: Charoenwet, Wachiraphan, et al.
Pubblicazione: (2023)

Improving Automated Code Reviews: Learning from Experience
di: Lin, Hong Yi, et al.
Pubblicazione: (2024)

Enhancing Code Review through Fuzzing and Likely Invariants
di: Charoenwet, Wachiraphan, et al.
Pubblicazione: (2025)

An Empirical Study of Static Analysis Tools for Secure Code Review
di: Charoenwet, Wachiraphan, et al.
Pubblicazione: (2024)

A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile
di: Pasuksmit, Jirat, et al.
Pubblicazione: (2024)

Adversarial Attacks on Code Models with Discriminative Graph Patterns
di: Nguyen, Thanh-Dat, et al.
Pubblicazione: (2023)

Functional Overlap Reranking for Neural Code Generation
di: To, Hung Quoc, et al.
Pubblicazione: (2023)

Automatically Recommend Code Updates: Are We There Yet?
di: Liu, Yue, et al.
Pubblicazione: (2022)

Can Code Language Models Learn Clarification-Seeking Behaviors?
di: Wu, Jie JW, et al.
Pubblicazione: (2025)

Correctness Assessment of Code Generated by Large Language Models Using Internal Representations
di: Bui, Tuan-Dung, et al.
Pubblicazione: (2025)

Large Language Models for Code Generation: A Comprehensive Survey of Challenges, Techniques, Evaluation, and Applications
di: Huynh, Nam, et al.
Pubblicazione: (2025)

Citation-Grounded Code Comprehension: Preventing LLM Hallucination Through Hybrid Retrieval and Graph-Augmented Context
di: Arafat, Jahidul
Pubblicazione: (2025)

GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents
di: Wu, Jie JW, et al.
Pubblicazione: (2025)

HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation
di: Tantithamthavorn, Kla, et al.
Pubblicazione: (2026)

CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
di: Lin, Hong Yi, et al.
Pubblicazione: (2025)

AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
di: Oliveira, Eduardo, et al.
Pubblicazione: (2026)

Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++
di: Nguyen, Anh The, et al.
Pubblicazione: (2024)

Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
di: Lin, Hong Yi, et al.
Pubblicazione: (2026)

Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
di: Li, Shuochuan, et al.
Pubblicazione: (2025)

When AI Models Become Dependencies: Studying the Evolution of Pre-Trained Model Reuse in Downstream Software Systems
di: Banyongrakkul, Peerachai, et al.
Pubblicazione: (2026)

From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers
di: Banyongrakkul, Peerachai, et al.
Pubblicazione: (2025)

Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration
di: Arani, Ali Kazemi, et al.
Pubblicazione: (2024)