35 citations · 76 across the 10 of their papers we have counts for
10 papers
What Survives the Next Model? Benchmarking LLM-Based Techniques Against Single-Prompts
Nahian Salsabil, Joy Saha, Simantika Bhattacharjee Dristi +4
The software engineering research community has enthusiastically embraced the integration of Large Language Models (LLMs) into complex techniques to solve a wide variety of tasks.…
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…
Pitfalls in Experiments with DNN4SE: An Analysis of the State of the Practice
Sira Vegas, Sebastian Elbaum
Software engineering techniques are increasingly relying on deep learning approaches to support many software engineering tasks, from bug triaging to code generation. To assess the…
PHYSFRAME: Type Checking Physical Frames of Reference for Robotic Systems
Sayali Kate, Michael Chinn, Hongjun Choi +2
A robotic system continuously measures its own motions and the external world during operation. Such measurements are with respect to some frame of reference, i.e., a coordinate sy…
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…
Self-Checking Deep Neural Networks in Deployment
Yan Xiao, Ivan Beschastnikh, David S. Rosenblum +4
The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make mistakes…
Probabilistic Conditional System Invariant Generation with Bayesian Inference
Meriel Stein, Sebastian Elbaum, Lu Feng +1
Invariants are a set of properties over program attributes that are expected to be true during the execution of a program. Since developing those invariants manually can be costly…
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…
A Language for Autonomous Vehicles Testing Oracles
Ana Nora Evans, Mary Lou Soffa, Sebastian Elbaum
Testing autonomous vehicles (AVs) requires complex oracles to determine if the AVs behavior conforms with specifications and humans' expectations. Available open source oracles are…
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…