35 citations · 93 across the 12 of their papers we have counts for
13 papers
Neural-Based Test Oracle Generation: A Large-scale Evaluation and Lessons Learned
Soneya Binta Hossain, Antonio Filieri, Matthew B. Dwyer +2
Defining test oracles is crucial and central to test development, but manual construction of oracles is expensive. While recent neural-based automated test oracle generation techni…
A DPLL(T) Framework for Verifying Deep Neural Networks
Hai Duong, ThanhVu Nguyen, Matthew Dwyer
Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-world problems. However, like human-written software, DNNs can have bugs and can be attacked. To…
PCV: A Point Cloud-Based Network Verifier
Arup Kumar Sarker, Farzana Yasmin Ahmad, Matthew B. Dwyer
3D vision with real-time LiDAR-based point cloud data became a vital part of autonomous system research, especially perception and prediction modules use for object classification,…
A Brief Survey on Oracle-based Test Adequacy Metrics
Soneya Binta Hossain, Matthew B. Dwyer
Code coverage is a popular and widespread test adequacy metric that measures the percentage of program codes executed by a test suite. Despite its popularity, code coverage has sev…
White-box Testing of NLP models with Mask Neuron Coverage
Arshdeep Sekhon, Yangfeng Ji, Matthew B. Dwyer +1
Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a numbe…
Algorithm Selection for Software Verification using Graph Neural Networks
Will Leeson, Matthew B Dwyer
The field of software verification has produced a wide array of algorithmic techniques that can prove a variety of properties of a given program. It has been demonstrated that the…
DNNV: A Framework for Deep Neural Network Verification
David Shriver, Sebastian Elbaum, Matthew B. Dwyer
Despite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, v…
Distribution-Aware Testing of Neural Networks Using Generative Models
Swaroopa Dola, Matthew B. Dwyer, Mary Lou Soffa
The reliability of software that has a Deep Neural Network (DNN) as a component is urgently important today given the increasing number of critical applications being deployed with…
Deep Learning & Software Engineering: State of Research and Future Directions
Prem Devanbu, Matthew Dwyer, Sebastian Elbaum +6
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was co…
Optimal Runtime Verification of Finite State Properties over Lossy Event Streams
Peeyush Kushwaha, Rahul Purandare, Matthew B. Dwyer
Monitoring programs for finite state properties is challenging due to high memory and execution time overheads it incurs. Some events if skipped or lost naturally can reduce both o…
Formal Language Constraints for Markov Decision Processes
Eleanor Quint, Dong Xu, Samuel Flint +2
In order to satisfy safety conditions, an agent may be constrained from acting freely. A safe controller can be designed a priori if an environment is well understood, but not when…
Refactoring Neural Networks for Verification
David Shriver, Dong Xu, Sebastian Elbaum +1
Deep neural networks (DNN) are growing in capability and applicability. Their effectiveness has led to their use in safety critical and autonomous systems, yet there is a dearth of…
SymInfer: Inferring Program Invariants using Symbolic States
ThanhVu Nguyen, Matthew B. Dwyer, Willem Visser
We introduce a new technique for inferring program invariants that uses symbolic states generated by symbolic execution. Symbolic states, which consist of path conditions and const…