1 citations · 5 across the 9 of their papers we have counts for
4 papers · 1 filter
MetaLint: Easy-to-Hard Generalization for Code Linting
Atharva Naik, Lawanya Baghel, Dhakshin Govindarajan +4
Large language models excel at code generation but struggle with code linting, particularly in generalizing to unseen or evolving best practices beyond those observed during traini…
CRScore++: Reinforcement Learning with Verifiable Tool and AI Feedback for Code Review
Manav Nitin Kapadnis, Atharva Naik, Carolyn Rose
Reinforcement learning (RL) to improve code review comment generation requires handling unstructured outputs, making reinforcement learning (RL) feedback challenging. The two main…
CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells
Atharva Naik, Marcus Alenius, Daniel Fried +1
The task of automated code review has recently gained a lot of attention from the machine learning community. However, current review comment evaluation metrics rely on comparisons…
CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
Yiqing Xie, Alex Xie, Divyanshu Sheth +3
To adequately test modern code generation systems, evaluation benchmarks must execute and test the code generated by the system. However, these execution and testing requirements h…