3 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
Do Not Copy/Paste: Soft Barriers for Copying in AI-Assisted Programming
Iyiola E. Olatunji, Alberick Euraste Djire, Jacques Klein +1
Copying a function from a chat window into an editor takes less than a second. For many uses of AI coding tools, that speed is the point; in settings such as programming education,…
Memorization Diagnostics for Code LLMs Should be Scale-Aware
Prateek Kumar Rajput, Abdoul Aziz Bonkoungou, Alberick Euraste Djiré +6
The extent to which large language models for code rely on memorization over genuine understanding remains highly debated. While current literature frequently reports widespread me…
Reinforcement Learning-Guided Chain-of-Draft for Token-Efficient Code Generation
Xunzhu Tang, Iyiola Emmanuel Olatunji, Tiezhu Sun +2
LLMs demonstrate surface-level fluency in code generation but struggle with structured reasoning tasks requiring correctness and semantic alignment. While Chain-of-Thought (CoT) pr…
Measuring LLM Code Generation Stability via Structural Entropy
Yewei Song, Tiezhu Sun, Xunzhu Tang +3
Assessing the stability of code generation from large language models (LLMs) is essential for judging their reliability in real-world development. We extend prior "structural-entro…
CodeAgent: Autonomous Communicative Agents for Code Review
Xunzhu Tang, Kisub Kim, Yewei Song +6
Code review, which aims at ensuring the overall quality and reliability of software, is a cornerstone of software development. Unfortunately, while crucial, Code review is a labor-…
Patch-CLIP: A Patch-Text Pre-Trained Model
Xunzhu Tang, Zhenghan Chen, Saad Ezzini +3
In recent years, patch representation learning has emerged as a necessary research direction for exploiting the capabilities of machine learning in software generation. These repre…