Deep learning for android malware defenses: a systematic literature review

Y Liu, C Tantithamthavorn, L Li, Y Liu - ACM Computing Surveys, 2022 - dl.acm.org
Malicious applications (particularly those targeting the Android platform) pose a serious
threat to developers and end-users. Numerous research efforts have been devoted to …

Linevul: A transformer-based line-level vulnerability prediction

M Fu, C Tantithamthavorn - … of the 19th International Conference on …, 2022 - dl.acm.org
Software vulnerabilities are prevalent in software systems, causing a variety of problems
including deadlock, information loss, or system failures. Thus, early predictions of software …

Refining chatgpt-generated code: Characterizing and mitigating code quality issues

Y Liu, T Le-Cong, R Widyasari… - ACM Transactions on …, 2024 - dl.acm.org
Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its
remarkable ability in language understanding and human-like responses. ChatGPT, based …

LineVD: statement-level vulnerability detection using graph neural networks

D Hin, A Kan, H Chen, MA Babar - Proceedings of the 19th international …, 2022 - dl.acm.org
Current machine-learning based software vulnerability detection methods are primarily
conducted at the function-level. However, a key limitation of these methods is that they do …

VulRepair: a T5-based automated software vulnerability repair

M Fu, C Tantithamthavorn, T Le, V Nguyen… - Proceedings of the 30th …, 2022 - dl.acm.org
As software vulnerabilities grow in volume and complexity, researchers proposed various
Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to …

Semi-supervised log-based anomaly detection via probabilistic label estimation

L Yang, J Chen, Z Wang, W Wang… - 2021 IEEE/ACM …, 2021 - ieeexplore.ieee.org
With the growth of software systems, logs have become an important data to aid system
maintenance. Log-based anomaly detection is one of the most important methods for such …

Using pre-trained models to boost code review automation

R Tufano, S Masiero, A Mastropaolo… - Proceedings of the 44th …, 2022 - dl.acm.org
Code review is a practice widely adopted in open source and industrial projects. Given the
non-negligible cost of such a process, researchers started investigating the possibility of …

Code prediction by feeding trees to transformers

S Kim, J Zhao, Y Tian, S Chandra - 2021 IEEE/ACM 43rd …, 2021 - ieeexplore.ieee.org
Code prediction, more specifically autocomplete, has become an essential feature in
modern IDEs. Autocomplete is more effective when the desired next token is at (or close to) …

Jitline: A simpler, better, faster, finer-grained just-in-time defect prediction

C Pornprasit… - 2021 IEEE/ACM 18th …, 2021 - ieeexplore.ieee.org
A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-
introducing. Recently, CC2Vec-a deep learning approach for Just-In-Time defect prediction …

A systematic literature review of explainable AI for software engineering

AH Mohammadkhani, NS Bommi, M Daboussi… - arxiv preprint arxiv …, 2023 - arxiv.org
Context: In recent years, leveraging machine learning (ML) techniques has become one of
the main solutions to tackle many software engineering (SE) tasks, in research studies …