Natural language generation and understanding of big code for AI-assisted programming: A review

MF Wong, S Guo, CN Hang, SW Ho, CW Tan - Entropy, 2023‏ - mdpi.com
This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

Large language models for education: A survey and outlook

S Wang, T Xu, H Li, C Zhang, J Liang, J Tang… - arxiv preprint arxiv …, 2024‏ - arxiv.org
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in
the realm of education. This survey paper summarizes the various technologies of LLMs in …

Large language models for software engineering: Survey and open problems

A Fan, B Gokkaya, M Harman… - 2023 IEEE/ACM …, 2023‏ - ieeexplore.ieee.org
This paper provides a survey of the emerging area of Large Language Models (LLMs) for
Software Engineering (SE). It also sets out open research challenges for the application of …

Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models

P Vaithilingam, T Zhang, EL Glassman - Chi conference on human …, 2022‏ - dl.acm.org
Recent advances in Large Language Models (LLM) have made automatic code generation
possible for real-world programming tasks in general-purpose programming languages …

Inferfix: End-to-end program repair with llms

M **, S Shahriar, M Tufano, X Shi, S Lu… - Proceedings of the 31st …, 2023‏ - dl.acm.org
Software development life cycle is profoundly influenced by bugs; their introduction,
identification, and eventual resolution account for a significant portion of software …

Retrieval-based prompt selection for code-related few-shot learning

N Nashid, M Sintaha, A Mesbah - 2023 IEEE/ACM 45th …, 2023‏ - ieeexplore.ieee.org
Large language models trained on massive code corpora can generalize to new tasks
without the need for task-specific fine-tuning. In few-shot learning, these models take as …

Copiloting the copilots: Fusing large language models with completion engines for automated program repair

Y Wei, CS **a, L Zhang - Proceedings of the 31st ACM Joint European …, 2023‏ - dl.acm.org
During Automated Program Repair (APR), it can be challenging to synthesize correct
patches for real-world systems in general-purpose programming languages. Recent Large …

Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation

Y Wang, W Wang, S Joty, SCH Hoi - arxiv preprint arxiv:2109.00859, 2021‏ - arxiv.org
Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently
shown to transfer well to Programming Languages (PL) and largely benefit a broad set of …

Examining zero-shot vulnerability repair with large language models

H Pearce, B Tan, B Ahmad, R Karri… - … IEEE Symposium on …, 2023‏ - ieeexplore.ieee.org
Human developers can produce code with cybersecurity bugs. Can emerging 'smart'code
completion tools help repair those bugs? In this work, we examine the use of large language …

Unified pre-training for program understanding and generation

WU Ahmad, S Chakraborty, B Ray… - arxiv preprint arxiv …, 2021‏ - arxiv.org
Code summarization and generation empower conversion between programming language
(PL) and natural language (NL), while code translation avails the migration of legacy code …