163 citations · 634 across the 44 of their papers we have counts for
20 papers · 1 filter
SkipFuzz: Active Learning-based Input Selection for Fuzzing Deep Learning Libraries
Hong Jin Kang, Pattarakrit Rattanukul, Stefanus Agus Haryono +4
Many modern software systems are enabled by deep learning libraries such as TensorFlow and PyTorch. As deep learning is now prevalent, the security of deep learning libraries is a…
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Tiezhu Sun, Kevin Allix, Kisub Kim +5
The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). Central to applying ML to software artifacts (like source or ex…
Duplicate Bug Report Detection: How Far Are We?
Ting Zhang, DongGyun Han, Venkatesh Vinayakarao +5
Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient c…
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets
Chen Gong, Zhou Yang, Yunpeng Bai +8
Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL…
Answer Summarization for Technical Queries: Benchmark and New Approach
Yang Chengran, Bowen Xu, Ferdian Thung +8
Prior studies have demonstrated that approaches to generate an answer summary for a given technical query in Software Question and Answer (SQA) sites are desired. We find that exis…
AutoPruner: Transformer-Based Call Graph Pruning
Thanh Le-Cong, Hong Jin Kang, Truong Giang Nguyen +4
Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise.…
Compressing Pre-trained Models of Code into 3 MB
Jieke Shi, Zhou Yang, Bowen Xu +2
Although large pre-trained models of code have delivered significant advancements in various code processing tasks, there is an impediment to the wide and fluent adoption of these…
Two Sides of the Same Coin: Exploiting the Impact of Identifiers in Neural Code Comprehension
Shuzheng Gao, Cuiyun Gao, Chaozheng Wang +3
Previous studies have demonstrated that neural code comprehension models are vulnerable to identifier naming. By renaming as few as one identifier in the source code, the models wo…
A Closer Look into Transformer-Based Code Intelligence Through Code Transformation: Challenges and Opportunities
Yaoxian Li, Shiyi Qi, Cuiyun Gao +4
Transformer-based models have demonstrated state-of-the-art performance in many intelligent coding tasks such as code comment generation and code completion. Previous studies show…
Automatic Pull Request Title Generation
Ting Zhang, Ivana Clairine Irsan, Ferdian Thung +3
Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging…
AutoPRTitle: A Tool for Automatic Pull Request Title Generation
Ivana Clairine Irsan, Ting Zhang, Ferdian Thung +2
With the rise of the pull request mechanism in software development, the quality of pull requests has gained more attention. Prior works focus on improving the quality of pull requ…
iTiger: An Automatic Issue Title Generation Tool
Ting Zhang, Ivana Clairine Irsan, Ferdian Thung +3
In both commercial and open-source software, bug reports or issues are used to track bugs or feature requests. However, the quality of issues can differ a lot. Prior research has f…
An Exploratory Study on Code Attention in BERT
Rishab Sharma, Fuxiang Chen, Fatemeh Fard +1
Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformer-based Pre-trained Language Models (PLM) trained on…
On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages
Fuxiang Chen, Fatemeh Fard, David Lo +1
A recent study by Ahmed and Devanbu reported that using a corpus of code written in multilingual datasets to fine-tune multilingual Pre-trained Language Models (PLMs) achieves high…
On the Effectiveness of Pretrained Models for API Learning
Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Ferdian Thung +4
Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit f…
Efficient Search of Live-Coding Screencasts from Online Videos
Chengran Yang, Ferdian Thung, David Lo
Programming videos on the Internet are valuable resources for learning programming skills. To find relevant videos, developers typically search online video platforms (e.g., YouTub…
Code Smells in Machine Learning Systems
Jiri Gesi, Siqi Liu, Jiawei Li +6
As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more compl…
PTM4Tag: Sharpening Tag Recommendation of Stack Overflow Posts with Pre-trained Models
Junda He, Bowen Xu, Zhou Yang +3
Stack Overflow is often viewed as the most influential Software Question Answer (SQA) website with millions of programming-related questions and answers. Tags play a critical role…
Natural Attack for Pre-trained Models of Code
Zhou Yang, Jieke Shi, Junda He +1
Pre-trained models of code have achieved success in many important software engineering tasks. However, these powerful models are vulnerable to adversarial attacks that slightly pe…
Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?
chengran Yang, Bowen Xu, Junaed younus Khan +4
APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers fr…