activity
20192022
most citedSymInfer: Inferring Program Invariants using Symbolic States

6 citations · 7 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL2022

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…

cs.SE2021

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…

cs.SE20201 cited

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…

cs.LO2020

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…

cs.LG2019

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…

cs.NE2019

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…