Posts in Software Engineering

PTM Naming: Why “What’s in a Name” Actually Matters for AI Reuse

I’m thrilled to share some recent work led by Wenxin Jiang, a PhD student at Purdue University. Wenxin is supervised by James C. Davis, and I have had the pleasure of serving as a key external supervisor and PhD committee member on this project as part of my ongoing collaboration with Dr. Davis. This research was recently accepted for publication in Journal of Empirical Software Engineering and it tackles a problem that anyone working in AI has likely grumbled about: how we name our models.

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SysLLMatic: Large Language Models as Software System Optimizers (2025)

This paper was led by Huiyun Peng and Akhil Gupte (Purdue University), working with James C. Davis, Yung-Hsiang Lu, and others including Ryan Hasler and Nicholas J. Eliopoulos. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2506.01249).

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Improving Deep Learning Reproducibility: A Case Study Investigation (2025)

This paper was led by Nadia Ravi and Aditya Goel (Purdue University), working with James C. Davis. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2505.03165).

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Special Issue on Low-Code/No-Code + Metaverse in IEEE Computer

Low-code/no-code is increasingly converging with the metaverse to reshape how we create and experience technology. As AI-driven tools automate significant portions of coding and power immersive virtual environments, organizations can achieve new efficiencies—provided that security, ethics, and user empowerment remain central.

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TLA+ for All: Running Model Checking in a Python Notebook

TLA+ has long been a powerful tool for designing and verifying complex systems. However, many students and practitioners have felt excluded by the ecosystem’s complexity, the need to install multiple tools, or the misconception that formal methods are only for specialists. This project aims to change that.

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A Signal Injection Attack Against Zero Involvement Pairing and Authentication for the Internet of Things

Zero Involvement Pairing and Authentication (ZIPA) is a technique for automatically provisioning large networks of Internet-of-Things (IoT) devices with no user involvement. Prior ZIPA work generally assumes that the environment used for pairing is sufficiently isolated from external, adversarial signals. In our DESTION 2024 paper (see Citation below), we present the first signal-injection attack capable of influencing ZIPA-based key generation, demonstrating that these assumptions can fail in realistic settings.

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What Do We Know About Hugging Face? A Systematic Literature Review (ESEM 2024)

This paper was led by Jason Jones and Wenxin Jiang (Purdue University, working with James C. Davis). I am one of the key leaders of this research project and contributed to the analysis and synthesis. The work appeared at ESEM 2024 (ACM/IEEE International Symposium on Empirical Software Engineering and Measurement).

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LLMs for Energy-Efficient Code: Emerging Results and Future Directions (2024)

This paper was led by Huiyun Peng and Akhil Gupte (Purdue University), working with James C. Davis and Yung-Hsiang Lu. I am one of the key leaders of this research project. The work is available as an arXiv preprint (arXiv:2410.09241).

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When ONNX Converters Fail: Interoperability Risks in Deep Learning (ISSTA 2024)

This paper was led by Purvish Jajal and Wenxin Jiang (Purdue University, working with James C. Davis and Yung-Hsiang Lu). I am one of the key leaders of this research project. The work appeared at ISSTA 2024 (ACM International Symposium on Software Testing and Analysis).

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PeaTMOSS: Mining Pre-Trained Models in Open-Source Software (MSR 2024)

This paper was led by Wenxin Jiang (Purdue University, working with James C. Davis). I am one of the key leaders of this research project and contributed to its design and analysis. The work appeared at MSR 2024 (International Conference on Mining Software Repositories).

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Deep Learning Model Reengineering: Challenges and Practices (EMSE 2024)

This paper was led by Wenxin Jiang (Purdue University, working with James C. Davis), with contributions from Vishnu Banna, Nikhil Vivek, Aditya Goel, and Nicholas Synovic. I am one of the key leaders of this research project. The work appeared in the Empirical Software Engineering journal.

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Pre-Trained Model Reuse in Hugging Face: An Empirical Study (ICSE 2023)

This paper was led by Wenxin Jiang, a PhD student at Purdue University working with James C. Davis. I am one of the key leaders of this research project, contributing to the study design, analysis, and framing. The work appeared at ICSE 2023, the flagship conference in software engineering.

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