I recently hosted an episode of Software Engineering Radio called "Will McGugan on Text-Based User Interfaces"!
🚀 Hi! I'm a researcher, teacher, podcaster, and software developer!
Innovating in technical areas such as software engineering and software testing, I teach courses, conduct research, write papers and a blog, give presentations, create software, and serve organizations. Working as an Associate Professor in the Department of Computer and Information Science at Allegheny College, I am an associate editor for the Journal of Software: Evolution and Process, an academic editor for the PeerJ Computer Science journal, a program committee member for conferences like the International Conference on Software Testing, Verification and Validation and the International Conference on Software Engineering, and a reviewer for journals like Transactions on Software Engineering and the Journal of Software Testing, Verification and Reliability. Along with media appearances on podcasts like Stack Overflow and Talk Python, I interview the world’s leading experts on software engineering as a co-host of Software Engineering Radio. You can learn more about me and my work by reading my biography, downloading my curriculum vitae, and subscribing to my mailing list.
Database Testing: Automatically test relational database schemas
Flaky Tests: Find and fix unpredictable and harmful test cases
Mutation Testing: Using automatically seeded defects to evaluate tests
Regression Testing: Efficiently and effectively rerunning test suites
Web Testing: Detecting and repairing responsive web page layout
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| Venue | Role(s) | Year(s) |
|---|---|---|
| International Flaky Tests Workshop | Program Committee Member | 2025 - 2024 |
| International Symposium on Software Testing and Analysis | Tool Demonstrations Program Committee | 2025 - 2024 |
| PeerJ Computer Science Journal | Academic Editor | 2025 - 2019 |
| Journal of Software: Evolution and Process | Associate Editor, Reviewer | 2025 - 2012 |
| International Workshop on Mutation Analysis | Program Committee Member | 2025 - 2010 |
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Algorithm Analysis: Implement and evaluate correct and efficient algorithms
Data Abstraction: Build and manipulate correct and efficient data structures
Discrete Structures: Clearly connect mathematics to Python programming
Operating Systems: Build and understand operating system components
Software Engineering: Team-based introduction to building software systems
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Cellveyor: Easily convey reports from Google Sheets to GitHub
Chasten: Configurable linting tool that uses XPath expressions
GatorGrade: Python front-end for the GatorGrader assessment tool
GatorGrader: Automated assessment for source code and writing
SchemaAnalyst: Data generation and mutation analysis for database schemas
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The trouble with Claude 4 in GitHub Copilot continues. It doesn't seem to constrain itself to the initial request and goes off in other directions for way too long. My experiment ends here. 3.7 from now on.
horrible weekend for weeyums :(
I was able to run the full version of the just-released DeepSeek R1-0528 on my M3 Ultra Mac Studio.
It worked…with 400 GB of RAM.
Here's how it went, along with some technical notes on the whole process for LM Studio and Ollama: https://www.macstories.net/notes/testing-deepseek-r1-0528-on-the-m3-ultra-mac-studio-and-installing-local-gguf-models-with-ollama-on-macos/
Wow! The Rust compiler correctly guessed the correct method I should be calling. Helpful and very impressive error handling!
@antoinechambertloir I'm not sure I understand the issue in 1): the conjectures listed in this database are all drawn from public sources, and are already part of the scientific commons. I see no reason why a private company should not be allowed to access this common resource, or to use that resource to build databases in a fashion that is non-exclusionary to other parties (in particular, the existence of this database does not prohibit other researchers from working with these conjectures without using the database).
2) Actually in recent years the fraction of mathematics that can be formalized in Lean's Mathlib has expanded significantly. Just to give one example, the notion of a perfectoid space was formalized back in 2019: https://leanprover-community.github.io/lean-perfectoid-spaces/ . While formalizing the _proofs_ of the core theory of mathematics is still far from reality, the goal of formalizing the majority of the _statements_ of key concepts and conjectures seems quite feasible to me within a few years (particularly with AI assistance).
In general, I would view these developments in positive-sum terms rather than zero-sum terms. If a contribution to mathematics is positive across all subfields, but has a stronger positive contribution in one subfield than in another, I would still view this as a net positive for mathematics, especially given that in time the subfields which could not immediately utilize the contribution can eventually figure out how to do so after interacting with those subfields that could initially benefit from it.