8 citations · 8 across the 11 of their papers we have counts for
11 papers
Empirical Study for Structured Output Control in LLMs for Software Engineering
Yewei Song, Prateek Rajput, Tiezhu Sun +3
LLM-generated outputs in software engineering rarely exist in isolation. They must plug into toolchains, APIs, and data pipelines that impose strict, often organization-specific st…
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…
MalLoc: Toward Fine-grained Android Malicious Payload Localization via LLMs
Tiezhu Sun, Marco Alecci, Aleksandr Pilgun +5
The rapid evolution of Android malware poses significant challenges to the maintenance and security of mobile applications (apps). Traditional detection techniques often struggle t…
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…
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Tiezhu Sun, Nadia Daoudi, Kisub Kim +3
Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graph…
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
Tiezhu Sun, Weiguo Pian, Nadia Daoudi +3
Transfomer-based models have significantly advanced natural language processing, in particular the performance in text classification tasks. Nevertheless, these models face challen…
Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification
Xunzhu Tang, Rujie Zhu, Tiezhu Sun +1
Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for Eng…
CKG: Dynamic Representation Based on Context and Knowledge Graph
Xunzhu Tang, Tiezhu Sun, Rujie Zhu +1
Recently, neural language representation models pre-trained on large corpus can capture rich co-occurrence information and be fine-tuned in downstream tasks to improve the performa…
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Tiezhu Sun, Kevin Allix, Kisub Kim +5
The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). Central to applying ML to software artifacts (like source or ex…
An Empirical Study of AI Techniques in Mobile Applications
Yinghua Li, Xueqi Dang, Haoye Tian +5
The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized servic…
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…