109 citations · 133 across the 23 of their papers we have counts for
19 papers · 1 filter
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…
MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing
Gong Chen, Wenjie Liu, Xiaoyuan Xie +3
Recently, several studies have indicated that data poisoning attacks pose a severe security threat to deep learning-based (DL-based) code search models. Attackers inject carefully…
Boosting Open-Source LLMs for Program Repair via Reasoning Transfer and LLM-Guided Reinforcement Learning
Xunzhu Tang, Jacques Klein, Tegawendé F. Bissyandé
Several closed-source LLMs have consistently outperformed open-source alternatives in program repair tasks, primarily due to their superior reasoning capabilities and extensive pre…
CallNavi, A Challenge and Empirical Study on LLM Function Calling and Routing
Yewei Song, Xunzhu Tang, Cedric Lothritz +6
API-driven chatbot systems are increasingly integral to software engineering applications, yet their effectiveness hinges on accurately generating and executing API calls. This is…
In-Context Code-Text Learning for Bimodal Software Engineering
Xunzhu Tang, Liran Wang, Yonghui Liu +6
Bimodal software analysis initially appeared to be within reach with the advent of large language models. Unfortunately, the complex interplay of natural language text and code in…
On the Diffusion of Test Smells in LLM-Generated Unit Tests
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the qua…
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-…
Enhancing Text-to-SQL Translation for Financial System Design
Yewei Song, Saad Ezzini, Xunzhu Tang +6
Text-to-SQL, the task of translating natural language questions into SQL queries, is part of various business processes. Its automation, which is an emerging challenge, will empowe…
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…
Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models
Liran Wang, Xunzhu Tang, Yichen He +4
Commit message generation (CMG) is a challenging task in automated software engineering that aims to generate natural language descriptions of code changes for commits. Previous me…
Hyperbolic Code Retrieval: A Novel Approach for Efficient Code Search Using Hyperbolic Space Embeddings
Xunzhu Tang, zhenghan Chen, Saad Ezzini +4
Within the realm of advanced code retrieval, existing methods have primarily relied on intricate matching and attention-based mechanisms. However, these methods often lead to compu…
Multilevel Semantic Embedding of Software Patches: A Fine-to-Coarse Grained Approach Towards Security Patch Detection
Xunzhu Tang, zhenghan Chen, Saad Ezzini +4
The growth of open-source software has increased the risk of hidden vulnerabilities that can affect downstream software applications. This concern is further exacerbated by softwar…
Learning to Represent Patches
Xunzhu Tang, Haoye Tian, Zhenghan Chen +6
Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed de…
Is ChatGPT the Ultimate Programming Assistant -- How far is it?
Haoye Tian, Weiqi Lu, Tsz On Li +4
Recently, the ChatGPT LLM has received great attention: it can be used as a bot for discussing source code, prompting it to suggest changes, provide descriptions or even generate c…
App Review Driven Collaborative Bug Finding
Xunzhu Tang, Haoye Tian, Pingfan Kong +3
Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., t…
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Haoye Tian, Xunzhu Tang, Andrew Habib +5
In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour.…
MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning
Weiguo Pian, Hanyu Peng, Xunzhu Tang +5
Representation learning of source code is essential for applying machine learning to software engineering tasks. Learning code representation from a multilingual source code datase…