[HTML][HTML] When llms meet cybersecurity: A systematic literature review

J Zhang, H Bu, H Wen, Y Liu, H Fei… - …, 2025 - cybersecurity.springeropen.com
The rapid development of large language models (LLMs) has opened new avenues across
various fields, including cybersecurity, which faces an evolving threat landscape and …

A new era in software security: Towards self-healing software via large language models and formal verification

N Tihanyi, R Jain, Y Charalambous, MA Ferrag… - arxiv preprint arxiv …, 2023 - arxiv.org
This paper introduces an innovative approach that combines Large Language Models
(LLMs) with Formal Verification strategies for automatic software vulnerability repair. Initially …

From llms to llm-based agents for software engineering: A survey of current, challenges and future

H **, L Huang, H Cai, J Yan, B Li, H Chen - arxiv preprint arxiv …, 2024 - arxiv.org
With the rise of large language models (LLMs), researchers are increasingly exploring their
applications in var ious vertical domains, such as software engineering. LLMs have …

Cref: An llm-based conversational software repair framework for programming tutors

B Yang, H Tian, W Pian, H Yu, H Wang, J Klein… - Proceedings of the 33rd …, 2024 - dl.acm.org
With the proven effectiveness of L arge L anguage M odels (LLMs) in code-related tasks,
researchers have explored their potential for program repair. However, existing repair …

Parameter-efficient fine-tuning in large models: A survey of methodologies

L Wang, S Chen, L Jiang, S Pan, R Cai, S Yang… - arxiv preprint arxiv …, 2024 - arxiv.org
The large models, as predicted by scaling raw forecasts, have made groundbreaking
progress in many fields, particularly in natural language generation tasks, where they have …

You Don't Have to Say Where to Edit! jLED–Joint Learning to Localize and Edit Source Code

W Pian, Y Li, H Tian, T Sun, Y Song, X Tang… - ACM Transactions on …, 2025 - dl.acm.org
Learning to edit code automatically is becoming more and more feasible. Thanks to recent
advances in Neural Machine Translation (NMT), various case studies are being investigated …

Resource-Efficient & Effective Code Summarization

S Afrin, J Call, KN Nguyen, O Chaparro… - arxiv preprint arxiv …, 2025 - arxiv.org
Code Language Models (CLMs) have demonstrated high effectiveness in automating
software engineering tasks such as bug fixing, code generation, and code documentation …

When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair

W Luo, JW Keung, B Yang, H Ye, CL Goues… - arxiv preprint arxiv …, 2024 - arxiv.org
Software systems have been evolving rapidly and inevitably introducing bugs at an
increasing rate, leading to significant losses in resources consumed by software …

LLM4CVE: Enabling Iterative Automated Vulnerability Repair with Large Language Models

M Fakih, R Dharmaji, H Bouzidi, GQ Araya… - arxiv preprint arxiv …, 2025 - arxiv.org
Software vulnerabilities continue to be ubiquitous, even in the era of AI-powered code
assistants, advanced static analysis tools, and the adoption of extensive testing frameworks …

Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair

Q Feng, X Ma, J Sheng, Z Feng, W Song… - arxiv preprint arxiv …, 2024 - arxiv.org
LLMs have garnered considerable attention for their potential to streamline Automated
Program Repair (APR). LLM-based approaches can either insert the correct code or directly …