16 citations · 94 across the 59 of their papers we have counts for
24 papers · 1 filter
TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation
Eric Spencer, Arslan Bisharat, Brian Ortiz +6
TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produce TLA+ specifications that fa…
TLA+-Bench: An Execution-Grounded Benchmark and Dataset for Natural-Language to TLA Specification Generation
Arslan Bisharat, Eric Spencer, Brian Ortiz +8
Large language models increasingly write TLA formal specifications from natural-language descriptions, but progress is hard to measure: existing resources grade by resemblanc…
Now's the Time: Computer Science Must Evolve to Emphasize Software and Systems Engineering with Artificial Intelligence (AI)
Chandra N. Sekharan, George K. Thiruvathukal
Computer science (CS) education needs to evolve to support software and artificial intelligence (AI) systems engineering, and it needs to happen now -- precisely because the core i…
Beyond Local Code Optimization: Multi-Agent Reasoning for Software System Optimization
Huiyun Peng, Parth Vinod Patil, Antonio Zhong Qiu +2
Large language models and AI agents have recently shown promise in automating software performance optimization, but existing approaches predominantly rely on local, syntax-driven…
An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process
Nicholas M. Synovic, Karolina Ryzka, Alessandra V. Vellucci Solari +3
Deep learning has achieved recognition for its impact within natural sciences, yet the prohibitive financial and technical cost of training models from scratch inhibit adoption. Fo…
Operationalizing Research Software for Supply Chain Security
Kelechi G. Kalu, Soham Rattan, Taylor R. Schorlemmer +3
Empirical studies of research software are hard to compare because the literature operationalizes ``research software'' inconsistently. Motivated by the research software supply ch…
Process-based Indicators of Vulnerability Re-Introducing Code Changes: An Exploratory Case Study
Samiha Shimmi, Nicholas M. Synovic, Mona Rahimi +1
Software vulnerabilities often persist or re-emerge even after being fixed, revealing the complex interplay between code evolution and socio-technical factors. While source code me…
SysLLMatic: Large Language Models are Software System Optimizers
Huiyun Peng, Arjun Gupte, Ryan Hasler +7
Automatic software system optimization can improve software speed, reduce operating costs, and save energy. Traditional approaches to optimization rely on manual tuning and compile…
Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
Huiyun Peng, Arjun Gupte, Nicholas John Eliopoulos +8
Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet o…
WIP: An Engaging Undergraduate Intro to Model Checking in Software Engineering Using TLA+
Konstantin Läufer, Gunda Mertin, George K. Thiruvathukal
Background: In this paper, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), in…
What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
Jason Jones, Wenxin Jiang, Nicholas Synovic +2
Background: Collaborative Software Package Registries (SPRs) are an integral part of the software supply chain. Much engineering work synthesizes SPR package into applications. Pri…
Reusing Deep Learning Models: Challenges and Directions in Software Engineering
James C. Davis, Purvish Jajal, Wenxin Jiang +3
Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive…
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
Wenxin Jiang, Jerin Yasmin, Jason Jones +6
The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their…
PeaTMOSS: Mining Pre-Trained Models in Open-Source Software
Wenxin Jiang, Jason Jones, Jerin Yasmin +6
Developing and training deep learning models is expensive, so software engineers have begun to reuse pre-trained deep learning models (PTMs) and fine-tune them for downstream tasks…
"I see models being a whole other thing": An Empirical Study of Pre-Trained Model Naming Conventions and A Tool for Enhancing Naming Consistency
Wenxin Jiang, Mingyu Kim, Chingwo Cheung +3
As innovation in deep learning continues, many engineers are incorporating Pre-Trained Models (PTMs) as components in computer systems. Some PTMs are foundation models, and others…
PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages
Wenxin Jiang, Nicholas Synovic, Purvish Jajal +5
Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downst…
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
Wenxin Jiang, Nicholas Synovic, Matt Hyatt +5
Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art ar…
Analysis of Failures and Risks in Deep Learning Model Converters: A Case Study in the ONNX Ecosystem
Purvish Jajal, Wenxin Jiang, Arav Tewari +6
Software engineers develop, fine-tune, and deploy deep learning (DL) models using a variety of development frameworks and runtime environments. DL model converters move models betw…
Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision
Wenxin Jiang, Vishnu Banna, Naveen Vivek +4
Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Dee…
Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics
Nicholas Synovic, Matt Hyatt, Rohan Sethi +11
Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. How…
An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors
Vishnu Banna, Akhil Chinnakotla, Zhengxin Yan +7
Machine learning techniques are becoming a fundamental tool for scientific and engineering progress. These techniques are applied in contexts as diverse as astronomy and spam filte…
Tests as Maintainable Assets Via Auto-generated Spies: A case study involving the Scala collections library's Iterator trait
Konstantin Läufer, John O'Sullivan, George K. Thiruvathukal
In testing stateful abstractions, it is often necessary to record interactions, such as method invocations, and express assertions over these interactions. Following the Test Spy d…
Metrics Dashboard: A Hosted Platform for Software Quality Metrics
George K. Thiruvathukal, Shilpika, Nicholas J. Hayward +1
There is an emerging consensus in the scientific software community that progress in scientific research is dependent on the "quality and accessibility of software at all levels" (…
Teaching Concurrent Software Design: A Case Study Using Android
Konstantin Läufer, George K. Thiruvathukal
In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile applicati…