38 citations · 103 across the 14 of their papers we have counts for
5 papers · 1 filter
Learning Defect Prediction from Unrealistic Data
Kamel Alrashedy, Vincent J. Hellendoorn, Alessandro Orso
Pretrained models of code, such as CodeBERT and CodeT5, have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensura…
Large Language Models for Test-Free Fault Localization
Aidan Z. H. Yang, Ruben Martins, Claire Le Goues +1
Fault Localization (FL) aims to automatically localize buggy lines of code, a key first step in many manual and automatic debugging tasks. Previous FL techniques assume the provisi…
CAT-LM: Training Language Models on Aligned Code And Tests
Nikitha Rao, Kush Jain, Uri Alon +2
Testing is an integral part of the software development process. Yet, writing tests is time-consuming and therefore often neglected. Classical test generation tools such as EvoSuit…
Skill over Scale: The Case for Medium, Domain-Specific Models for SE
Manisha Mukherjee, Vincent J. Hellendoorn
Recent advancements in AI have sparked a trend in constructing large, generalist language models that handle a multitude of tasks, including many code-related ones. While these mod…
AI for Low-Code for AI
Nikitha Rao, Jason Tsay, Kiran Kate +2
Low-code programming allows citizen developers to create programs with minimal coding effort, typically via visual (e.g. drag-and-drop) interfaces. In parallel, recent AI-powered t…