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Strange Loop Conference | Mei Nagappan: Bias in evaluating code contributions @StrangeLoopConf | Uploaded May 2022 | Updated October 2024, 1 week ago.
Mei is an Associate Professor at the University of Waterloo. His talk looks at how perceptions of contributors' race and ethnicity affects the way their code changes are evaluated.

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On April 27, 2022, It Will Never Work in Theory ran its first live event: lightning talks from leading software engineering researchers presenting immediate, actionable results from their work. Our audience learned:
- powerful new ways to test modern software
- how to do better, smarter code reviews,
- what effective remote onboarding means during the pandemic,
- whether test-driven development actually makes you more productive,
- and what "productive" really means for programmers.

Their slides, and over 250 reviews of software engineering research papers, are all available on https://neverworkintheory.org.

We are grateful to Strange Loop, Mozilla, and Taylor & Francis for their support, and we hope you'll join us at Strange Loop 2022 in September for more insights.
Mei Nagappan: Bias in evaluating code contributionsPolymorphism Unbound by Bruce Eckel (Strange Loop 2022)Open-Sourcing Venice by Felix GV (Strange Loop 2022)Designing Dope Distributed Systems for Outer Space with High-Fidelity Simulation by Toby BellKelly Blincoe: The effects of destructive criticism in code reviewDavide Fucci: The hidden costs and benefits of TDDAndy Zaidman: Programmer by day, tester by nightOne Thousand and One Stories: a Large-scale Survey of Software Refactoring by Mohamed Wiem MkaouerProgram Synthesis: A Dream Realized? by Roopsha SamantaWhat We Learned Dissecting the Worlds Most Popular Containers by Ayse Kaya (Strange Loop 2022)Strange Loop Chat with África Periañez about predictive models and benshi.aiClient Side Deep Learning Optimization with PyTorch by Tyler Kirby and Shane Caldwell

Mei Nagappan: Bias in evaluating code contributions @StrangeLoopConf

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