54 citations · 73 across the 9 of their papers we have counts for
9 papers
Event-B Agent: Towards LLM Agent for Formal Model Synthesis and Repair
Hongshu Wang, Xinyue Zuo, Yuhan Sun +3
Building software that is correct by construction is a long-standing goal in software engineering, as it ensures reliability during design and development rather than after deploym…
Generalizing Test Cases for Comprehensive Test Scenario Coverage
Binhang Qi, Yun Lin, Xinyi Weng +4
Test cases are essential for software development and maintenance. In practice, developers derive multiple test cases from an implicit pattern based on their understanding of requi…
GUI Test Migration via Abstraction and Concretization
Yakun Zhang, Chen Liu, Xiaofei Xie +4
GUI test migration aims to produce test cases with events and assertions to test specific functionalities of a target app. Existing migration approaches typically focus on the widg…
CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature
Chenyan Liu, Yufan Cai, Yun Lin +6
Recent years have seen the development of LLM-based code generation. Compared to generating code in a software project, incremental code edits are empirically observed to be more f…
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
Mark Huasong Meng, Guangdong Bai, Sin Gee Teo +4
Neural networks have been widely applied in security applications such as spam and phishing detection, intrusion prevention, and malware detection. This black-box method, however,…
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Yuting Yang, Pei Huang, Juan Cao +5
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerab…
Generalizing Neural Networks by Reflecting Deviating Data in Production
Yan Xiao, Yun Lin, Ivan Beschastnikh +3
Trained with a sufficiently large training and testing dataset, Deep Neural Networks (DNNs) are expected to generalize. However, inputs may deviate from the training dataset distri…
RegMiner: Towards Constructing a Large Regression Dataset from Code Evolution History
Xuezhi Song, Yun Lin, Siang Hwee Ng +4
Bug datasets consisting of real-world bugs are important artifacts for researchers and programmers, which lay empirical and experimental foundation for various SE/PL research such…
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…