ASSET Research Lab - Purdue University

The ASSET Research Lab at Purdue University led by Dr. Lin Tan focuses on advancing the synergy between software engineering and artificial intelligence, with the goal of building more reliable, secure, and intelligent software systems. Our research tackles critical challenges in software development by leveraging emerging technologies such as large language models (LLMs), machine learning, and deep learning, alongside foundational techniques in coding, testing, and security.

Our Research Areas

Research Areas

AI for Software Engineering (AI4SE)

LLMs for code generation and understanding

Binary analysis and recovery

LLMs for robotics navigation and constrained decoding

Automated program repair and vulnerability fixing

Testing Deep Learning Systems

Testing and validating deep learning models and libraries

Cross-backend validation to detect bugs in DL libraries

Automated techniques for improving DL systems reliability

Dependable Distributed Systems

Detecting and fixing bugs in distributed systems

Analyzing vicious cycles in distributed software

Our Team

Meet the our team members

Dr. Lin Tan

Mary J. Elmore New Frontiers Professor

Lab Director

Shangshu Qian

Ph.D. Student

Research in dependable distributed systems

Yi Wu

Ph.D. Student

Research in AI4SE

Shanchao Liang

Ph.D. Student

Research in AI4SE

Yiran Hu

Ph.D. Student

Research in AI4SE

Kevin Zhang

Ph.D. Student

AI explainability & Robotics

Anik Dey

Ph.D. Student

Research in LLM Inference and Safety

Qi Li

Ph.D. Student

Research in AI4SE

Recent Publications

Can Language Models Replace Programmers for Coding? REPOCOD Says 'Not Yet'

Shanchao Liang, Nan Jiang, Yiran Hu, Lin Tan

ACL 2025 Main

PDF Dataset

WAFFLE: Fine-tuning Multi-Modal Model for Automated Front-End Development

Shanchao Liang, Nan Jiang, Shangshu Qian, Lin Tan

ACL 2025 Main

PDF Code

SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models

Yi Wu, Zikang Xiong, Yiran Hu, Shreyash Iyengar, Nan Jiang, Aniket Bera, Lin Tan, and Suresh Jagannathan

ICRA 2025 - Best Paper Award Finalist!

PDF

Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning

Nan Jiang, Chengxiao Wang, Kevin Liu, Xiangzhe Xu, Lin Tan, Xiangyu Zhang, and Petr Babkin

ICLR 2025

PDF Model

ReSym: Harnessing LLMs to Recover Variable and Data Structure Symbols from Stripped Binaries

Danning Xie, Zhuo Zhang, Nan Jiang, Xiangzhe Xu, Lin Tan, and Xiangyu Zhang

CCS 2024 - Distinguished Paper Award!

PDF Code

View All Publications

Software & Tools

Open-source contributions from our lab

RepoCod

A complex benchmark for repository-level code generation.

Link

SELP

a framework designed to make LLM-based planners more reliable when generating action plans for robot agents.

Link

NOVA

Foundation models for binary code and fine-tuned models for binary code.

HuggingFace

LATTE

Improving LaTeX recognition for tables and formulae with iterative refinement

Dataset

ReSym

Tool for recovering variable and data structure symbols from stripped binaries using LLMs

GitHub

More projects are available at our software page.

Alumni

Our Valued Former Members

PhD and Master's Students:

Nan Jiang (PhD 2025, First Employment: Microsoft)
Jiannan Wang (PhD 2025, First Employment: Meta)
Danning Xie (PhD 2025, co-advised by Xiangyu Zhang; First Employment: Meta)
Hung Pham (PhD, August 2022, Tenure-track Assistant Professor at York University)
Thibaud Lutellier (PhD, December 2020, Tenure-track Assistant Professor at University of Alberta)
Song Wang (PhD, December 2018, tenured Associate Professor at York University)
Edmund Wong (PhD, December 2018, First Employment: Caffeine TV)
Jinqiu Yang (PhD, April 2018, tenured Associate Professor at Concordia University)

Show Full List of Alumni

Join Our Lab

Opportunities for students and researchers

We are always looking for passionate and talented individuals to join our research team. Whether you're a prospective Ph.D. student, postdoctoral researcher, or undergraduate student interested in research, we'd love to hear from you.

Please send Lin an email if you are interested in joining our Lab. In your email, please clearly state that you have read the instructions on this page.

Current Openings

Postdoctoral Position

A postdoctoral position is available (start time negotiable). Applicants with expertise in software reliability, text analytics, bug prediction/detection/repair, program comprehension, program synthesis, mining software repositories, and empirical studies are encouraged to apply.

Requirements:

Ph.D. in software engineering or related field (or near completion)
Strong publication record
Excellent programming skills

Ph.D. and Master's Positions

We have funded openings for highly motivated graduate students. Please include your resume, transcripts, research ideas, and code samples (e.g., GitHub link). Experience in LLMs, binary recovery, or decompilation is a plus.

Undergraduate Research Opportunities

Open to students with interest in software engineering or AI. Both funded and volunteer positions available. Please send your resume, transcripts, and code samples, along with your research interests.

If you're interested in joining our lab, please contact us with your CV and a brief statement of your research interests.