JWare Lab

JWare Lab (杰件实验室) is affiliated with the Department of Computer Science at Michigan Technological University. The name JWare is inspired by the PI’s first name, Jie (pronounced “jyeh”), a character meaning greatness. This reflects our aspiration: that software, built with the right foundations, can be truly great. We also aspire to empower students with the foundations to build great software in their future careers — in an era increasingly shaped by Generative AI.

We investigate the quality foundations, principles, and methodologies that make AI-assisted software engineering rigorous and trustworthy. Our research spans the full software development lifecycle: design, coding, testing, review, deployment, and maintenance. We are committed to ensuring that AI tools elevate the quality and safety of software, rather than erode it.

Lab Members

Faculty

Dr. Jie Wu Principal Investigator

PhD Students

Joshana Shakya PhD Student (incoming Fall 2026)

Indrajeet Roy PhD Student (incoming Fall 2026)

Undergraduate Students

Gabe Dautovi Undergraduate Student

Andrew Sadler Undergraduate Student

News

Jun 2026 — Indrajeet Roy and Joshana Shakya join as incoming PhD students (Fall 2026)
Jun 2026 — JWare Lab website was launched
Jun 2026 — Research positions open for undergrad and graduate students — Summer & Fall 2026

Join Us

We are actively recruiting undergraduate and graduate students at Michigan Tech. See our open positions for details.

Preprint

MANTRA

MANTRA: a Framework for Multi-stage Adaptive Noise TReAtment During Training

Shawn Zhao, Fatemeh H. Fard, Jie JW Wu

ArXiv @article{zhao2025mantra, title={MANTRA: a Framework for Multi-stage Adaptive Noise TReAtment During Training}, author={Zhao, Shawn and Fard, Fatemeh H. and Wu, Jie JW}, journal={arXiv preprint arXiv:2512.04319}, year={2025} }

(Under Review)
ClarifyCoder

Can Code Language Models Learn Clarification-Seeking Behaviors?

Jie JW Wu, Manav Chaudhary, Davit Abrahamyan, Arhaan Khaku, Anjiang Wei, Fatemeh H. Fard

Arxiv Code Video @article{wu2025clarifycoder, title={Can Code Language Models Learn Clarification-Seeking Behaviors?}, author={Wu, Jie JW and Chaudhary, Manav and Abrahamyan, Davit and Khaku, Arhaan and Wei, Anjiang and Fard, Fatemeh H.}, journal={arXiv preprint arXiv:2504.16331}, year={2025} }

(under review)
MergeRepair

MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair

Meghdad Dehghan, Jie JW Wu, Fatemeh Hendijani Fard, Ali Ouni

Arxiv Code @article{dehghan2024mergerepair, title={MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair}, author={Dehghan, Meghdad and Wu, Jie JW and Fard, Fatemeh H and Ouni, Ali}, journal={arXiv preprint arXiv:2408.09568}, year={2024} }

(Accepted RR. Under Review)

Publications

MetaMorphic Testing

Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study

Jie JW Wu, Feiyu E, Bo Chen

Coming Soon

AIware 2026 (Arxiv Track)
GrowthHacker

GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents

Jie JW Wu*, Ayanda Patrick Herlihy*, Ahmad Saleem Mirza, Ali Afoud, Fatemeh Fard (*Equal Contribution)

Paper Code @article{wuherlihy2025growthhacker, title={GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents}, author={Wu, Jie JW and Herlihy, Ayanda Patrick and Mirza, Ahmad Saleem and Afoud, Ali and Fard, Fatemeh}, journal={ACM Transactions on Software Engineering and Methodology}, year={2026} }

ACM Transactions on Software Engineering and Methodology (TOSEM), 2026
FISCAL

FISCAL: Financial Synthetic Claim–document Augmented Learning for Efficient Fact-Checking

Rishab Sharma, Iman Saberi, Elham Alipour, Jie JW Wu, Fatemeh H. Fard

Arxiv Code @inproceedings{sharma2025fiscal, title={FISCAL: Financial Synthetic Claim–document Augmented Learning for Efficient Fact-Checking}, author={Sharma, Rishab and Saberi, Iman and Alipour, Elham and Wu, Jie JW and Fard, Fatemeh H.}, booktitle={NeurIPS 2025 Workshop on GenAI in Finance}, year={2025} }

NeurIPS 2025 Workshop "GenAI in Finance".
Technique deployed in Charli AI’s financial production system.
LLM Survey on Low Resource PL

A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages

Sathvik Joel*, Jie JW Wu*, Fatemeh Hendijani Fard
(*Equal contribution)

GitHub @article{joel2024survey, title={A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages}, author={Joel, Sathvik and Wu, Jie JW and Fard, Fatemeh H}, journal={arXiv preprint arXiv:2410.03981v3}, year={2024} }

ACM Transactions on Software Engineering and Methodology (TOSEM), 2025
LLM Communication Benchmark

HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent

Jie JW Wu, Fatemeh Hendijani Fard

Code Poster Video @article{wu2025benchmarking, author = {Wu, Jie JW and Fard, Fatemeh H.}, title = {HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent}, journal = {ACM Trans. Softw. Eng. Methodol.}, year = {2025}, doi = {10.1145/3715109}, url = {https://doi.org/10.1145/3715109} }

ACM Transactions on Software Engineering and Methodology (TOSEM), 2025
Autmoated Offline AB

AutoOffAB: Toward Automated Offline A/B Testing for Data-Driven Requirement Engineering

Jie JW Wu

32nd ACM International Conference on the Foundations of Software Engineering (FSE ’24, Ideas, Visions and Reflections Track)

Arxiv @inproceedings{wu2024autooffab, title={AutoOffAB: Toward Automated Offline A/B Testing for Data-Driven Requirement Engineering}, author={Wu, Jie JW}, booktitle={Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering}, pages={472--476}, year={2024} }
Code Clone Detection

Investigating the Efficacy of Large Language Models for Code Clone Detection

Mohamad Khajezade, Jie JW Wu, Fatemeh Hendijani Fard, Gema Rodríguez-Pérez,Mohamed Sami Shehata

International Conference on Program Comprehension (ICPC 2024), Early Research Achievements (ERA) (short paper)

Arxiv @inproceedings{khajezade2024investigating, title={Investigating the Efficacy of Large Language Models for Code Clone Detection}, author={Khajezade, Mohamad and Wu, Jie JW and Fard, Fatemeh Hendijani and Rodr{\'\i}guez-P{\'e}rez, Gema and Shehata, Mohamed Sami}, booktitle={Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension}, pages={161--165}, year={2024} }
V-Model

An Exploratory Study of V-Model in Building ML-Enabled Software: A Systems Engineering Perspective

Jie JW Wu

3rd International Conference on AI Engineering – Software Engineering for AI (CAIN 2024) (long paper) Distinguished Paper Award Candidate.

Slides Blog @inproceedings{wu2024exploratory, title={An Exploratory Study of V-Model in Building ML-Enabled Software: A Systems Engineering Perspective}, author={Wu, Jie JW}, booktitle={3rd International Conference on AI Engineering – Software Engineering for AI (CAIN 2024)}, note={Lisbon, Portugal} }
LLM Communication

Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code

Jie JW Wu

The 7th Annual Symposium on Machine Programming (MAPS ’23 Workshop)

Slides @inproceedings{wu2023large, title={Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code}, author={Wu, Jie JW}, booktitle={The 7th Annual Symposium on Machine Programming (MAPS ’23 Workshop)}, note={Workshop, December 3, 2023, San Francisco, CA, USA} }
Dissertation

Towards Formalizing Data-Driven Decision-Making from Big Data: A Systematic Multi-Criteria Decision-Making Approach in Online Controlled Experiments

Jie JW Wu

Ph.D. Dissertation

Slides @phdthesis{wu2023towards, title={Towards Formalizing Data-Driven Decision-Making from Big Data: A Systematic Multi-Criteria Decision-Making Approach in Online Controlled Experiments}, author={Wu, Jie JW}, year={2023}, school={The George Washington University} }
MOVSW

A Multi-Objective Evolutionary Approach Towards Automated Online Controlled Experiments

Jie JW Wu, Thomas A. Mazzuchi, and Shahram Sarkani

Journal of Systems and Software, 2023.

Pre-print Code @article{wu2023multi, title={A multi-objective evolutionary approach towards automated online controlled experiments}, author={Wu, Jie JW and Mazzuchi, Thomas A and Sarkani, Shahram}, journal={Journal of Systems and Software}, pages={111703}, year={2023}, publisher={Elsevier} }
LDM

Comparison of Multi-Criteria Decision-Making Methods for Online Controlled Experiments in a Launch Decision-Making Framework

Jie JW Wu, Thomas A. Mazzuchi, and Shahram Sarkani

Information and Software Technology 155 (2023): 107115.

Pre-print Code Slides @article{wu2023comparison, title={Comparison of multi-criteria decision-making methods for online controlled experiments in a launch decision-making framework}, author={Wu, Jie JW and Mazzuchi, Thomas A and Sarkani, Shahram}, journal={Information and Software Technology}, volume={155}, pages={107115}, year={2023}, publisher={Elsevier} }
Offline Sketch Parsing

Offline Sketch Parsing via Shapeness Estimation

Jie Wu, Changhu Wang, Liqing Zhang, Yong Rui

IJCAI. Vol. 15. 2015. (long paper, acceptance rate:28.6%)
Online Sketch Recognition

Sketch Recognition with Natural Correction and Editing

Jie Wu, Changhu Wang, Liqing Zhang, Yong Rui

Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 28. No. 1. 2014. (long paper, acceptance rate:28.0%)

Video
Gestalt Saliency

Gestalt saliency: Salient region detection based on gestalt principles

Jie Wu, Liqing Zhang

2013 IEEE International Conference on Image Processing. IEEE, 2013. Top 1% Excellent Undergraduate Thesis Award at SJTU

← Back to Homepage