Joshua Kaplan is a mobile-focused software engineer with nine years of experience building reliable iOS infrastructure and tooling, currently contributing to Netflix's iOS infra after roles at TikTok, Mercari US, and GMO Pepabo. He pairs production experience with a strong open-source track record—contributing tests and improvements to prominent Swift projects like SwiftLint, SourceKit-LSP, and Danger—to ensure correctness and modern Swift practices. Bilingual in English and Japanese, he brings cross-cultural communication skills honed in prior international roles and a BA in East Asian Studies. Known for pragmatic test automation and careful refactors, Joshua blends backend language-server work with client-side expertise to make developer tooling and mobile platforms more robust.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Arts (BA), East Asian Studies, Bachelor of Arts (BA), East Asian Studies at Lewis and Clark College
Contributions:38 commits, 4 PRs, 39 comments in 6 months
Contributions summary:Joshua's primary contribution involves writing unit tests for various Swift code components within the `danger/swift` repository. Their work includes creating test cases for `GitHubUser`, `GitHubMilestone`, and other data structures, verifying their correct decoding from JSON. The user has also refactored existing tests and added a new test suite for a date formatter extension. The commits demonstrate a focus on ensuring the correctness and reliability of the project's data models and functionality.
Contributions:14 commits, 3 PRs, 10 comments in 17 days
Contributions summary:Joshua primarily contributed to the test suite of the project. They added tests for a new rule, which included defining test cases and verifying rule behavior. Furthermore, they updated test configurations and test files, renaming some rules to legacy versions while updating the test structure to match the changes. This indicates a focus on ensuring the correctness and reliability of the codebase through automated testing.
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