Search Results for author:
Found 5 papers, 2 papers with code
Correctness Assessment of Code Generated by Large Language Models Using Internal Representations
Ensuring the correctness of code generated by Large Language Models (LLMs) presents a significant challenge in AI-driven software development.
Generating Critical Scenarios for Testing Automated Driving Systems
In this paper, we propose AVASTRA, a Reinforcement Learning (RL)-based approach to generate realistic critical scenarios for testing ADSs in simulation environments.
RAMBO: Enhancing RAG-based Repository-Level Method Body Completion
Instead of retrieving similar method bodies, RAMBO identifies essential repository-specific elements, such as classes, methods, and variables/fields, and their relevant usages.
Ranked #1 on
Code Completion
on Rambo Benchmark
An Empirical Study on Capability of Large Language Models in Understanding Code Semantics
In addition, the code LLMs exhibit better robustness to the semantic preserving transformations than their sensitivity to the semantic non-preserving transformations.
ARIST: An Effective API Argument Recommendation Approach
To implement this idea in the recommendation process, ARIST combines program analysis (PA), language models (LMs), and several features specialized for the recommendation task which consider the functionality of formal parameters and the positional information of code elements (e. g., variables or method calls) in the given context.
