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 …, 2023 - dl.acm.org
Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

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 …

Codet5+: Open code large language models for code understanding and generation

Y Wang, H Le, AD Gotmare, NDQ Bui, J Li… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) pretrained on vast source code have achieved prominent
progress in code intelligence. However, existing code LLMs have two main limitations in …

Unixcoder: Unified cross-modal pre-training for code representation

D Guo, S Lu, N Duan, Y Wang, M Zhou… - arXiv preprint arXiv …, 2022 - arxiv.org
Pre-trained models for programming languages have recently demonstrated great success
on code intelligence. To support both code-related understanding and generation tasks …

Reacc: A retrieval-augmented code completion framework

S Lu, N Duan, H Han, D Guo, S Hwang… - arXiv preprint arXiv …, 2022 - arxiv.org
Code completion, which aims to predict the following code token (s) according to the code
context, can improve the productivity of software development. Recent work has proved that …

An empirical comparison of pre-trained models of source code

C Niu, C Li, V Ng, D Chen, J Ge… - 2023 IEEE/ACM 45th …, 2023 - ieeexplore.ieee.org
While a large number of pre-trained models of source code have been successfully
developed and applied to a variety of software engineering (SE) tasks in recent years, our …

An empirical study of deep learning models for vulnerability detection

B Steenhoek, MM Rahman, R Jiles… - 2023 IEEE/ACM 45th …, 2023 - ieeexplore.ieee.org
Deep learning (DL) models of code have recently reported great progress for vulnerability
detection. In some cases, DL-based models have outperformed static analysis tools …

BERT-and TF-IDF-based feature extraction for long-lived bug prediction in FLOSS: a comparative study

L Gomes, R da Silva Torres, ML Côrtes - Information and Software …, 2023 - Elsevier
Context: The correct prediction of long-lived bugs could help maintenance teams to build
their plan and to fix more bugs that often adversely affect software quality and disturb the …

Multi-target backdoor attacks for code pre-trained models

Y Li, S Liu, K Chen, X Xie, T Zhang, Y Liu - arXiv preprint arXiv …, 2023 - arxiv.org
Backdoor attacks for neural code models have gained considerable attention due to the
advancement of code intelligence. However, most existing works insert triggers into task …

Machine/deep learning for software engineering: A systematic literature review

S Wang, L Huang, A Gao, J Ge, T Zhang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …