Large language models for software engineering: A systematic literature review

X Hou, Y Zhao, Y Liu, Z Yang, K Wang, L Li… - ACM Transactions on …, 2024 - dl.acm.org
Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

Machine learning for actionable warning identification: A comprehensive survey

X Ge, C Fang, X Li, W Sun, D Wu, J Zhai, SW Lin… - ACM Computing …, 2024 - dl.acm.org
Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static
code analyzers. With recent advances in Machine Learning (ML), various approaches have …

Robustness, security, privacy, explainability, efficiency, and usability of large language models for code

Z Yang, Z Sun, TZ Yue, P Devanbu, D Lo - arxiv preprint arxiv:2403.07506, 2024 - arxiv.org
Large language models for code (LLM4Code), which demonstrate strong performance (eg,
high accuracy) in processing source code, have significantly transformed software …

{VulSim}: Leveraging Similarity of {Multi-Dimensional} Neighbor Embeddings for Vulnerability Detection

S Shimmi, A Rahman, M Gadde, H Okhravi… - 33rd USENIX Security …, 2024 - usenix.org
Despite decades of research in vulnerability detection, vulnerabilities in source code remain
a growing problem, and more effective techniques are needed in this domain. To enhance …

SpecEval: Evaluating Code Comprehension in Large Language Models via Program Specifications

L Ma, S Liu, L Bu, S Li, Y Wang, Y Liu - arxiv preprint arxiv:2409.12866, 2024 - arxiv.org
Large Language models have achieved impressive performance in automated software
engineering. Extensive efforts have been made to evaluate the abilities of code LLMs in …

Open-source AI-based SE tools: opportunities and challenges of collaborative software learning

Z Lin, W Ma, T Lin, Y Zheng, J Ge, J Wang… - ACM Transactions on …, 2024 - dl.acm.org
Large Language Models (LLMs) have become instrumental in advancing software
engineering (SE) tasks, showcasing their efficacy in code understanding and beyond. AI …

Attention is all you need for llm-based code vulnerability localization

Y Li, X Li, H Wu, Y Zhang, X Cheng, S Zhong… - arxiv preprint arxiv …, 2024 - arxiv.org
The rapid expansion of software systems and the growing number of reported vulnerabilities
have emphasized the importance of accurately identifying vulnerable code segments …

An empirical study on capability of large language models in understanding code semantics

TT Nguyen, TT Vu, HD Vo, S Nguyen - arxiv preprint arxiv:2407.03611, 2024 - arxiv.org
Large Language Models for Code (code LLMs) have demonstrated remarkable performance
across various software engineering (SE) tasks, increasing the application of code LLMs in …

A Survey on Large Language Models for Code Generation

J Jiang, F Wang, J Shen, S Kim, S Kim - arxiv preprint arxiv:2406.00515, 2024 - arxiv.org
Large Language Models (LLMs) have garnered remarkable advancements across diverse
code-related tasks, known as Code LLMs, particularly in code generation that generates …

AIGC 大模型测评综述: 使能技术, 安全隐患和应对.

许志伟, **海龙, **博, **涛, 王嘉泰… - Journal of Frontiers …, 2024 - search.ebscohost.com
人工智能生成内容(AIGC) 模型因出色的内容生成能力, 在全球范围内引起了广泛关注与应用.
然而AIGC 大模型的快速发展也带来了一系列隐患, 例如模型生成结果的可解释性 …