28 citations · 35 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…