35 citations · 93 across the 12 of their papers we have counts for
6 papers · 1 filter
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 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…
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…
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…
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…