opensafely-core/ehrql
Nov 2024 – Dec 2024
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentCode CoverageCode IntegrationCode RefactoringConfigurationData Generation
David R. MacIver
PROFILE
David R. Maciver
Contributed to the opensafely-core/ehrql repository by building and refining a robust dummy data generation and generative testing framework over two months. Focused on backend development using Python, the work introduced deterministic random seeding, realistic data constraints, and per-instance configuration to ensure reproducible and diverse test data. Enhanced test coverage and reliability by integrating edge-case scenarios, retry logic for categorical data, and improved exception handling. Code refactoring and dead code elimination simplified execution paths, improving maintainability and onboarding. Leveraged tools such as Pytest and Hypothesis to strengthen test-driven development practices, resulting in a more reliable and maintainable codebase.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
21Total
Bugs
0
Commits
21
Features
4
Lines of code
2,213
Activity Months2
Your Network
13 peopleWork History
December 2024
10 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for opensafely-core/ehrql focusing on delivering realistic, diverse, and reproducible dummy data, while tightening test reliability and code quality. Key contributions span feature work in dummy data generation and test coverage improvements, with targeted fixes to randomness handling and data constraints.
10 Commits • 2 Features
Dec 1, 2024December 2024 performance summary for opensafely-core/ehrql focusing on delivering realistic, diverse, and reproducible dummy data, while tightening test reliability and code quality. Key contributions span feature work in dummy data generation and test coverage improvements, with targeted fixes to randomness handling and data constraints.
December 2024
November 2024
11 Commits • 2 Features
Nov 1, 2024November 2024 — Delivered substantial improvements to the generative testing framework and dummy data generation, and performed targeted code cleanup in the query model. These efforts increased test data realism and reliability, reduced maintenance burden, and laid groundwork for future features. Key actions included integrating the nextgen dummy data generator with deterministic RNG seeding and refined event-data distributions, reintroducing edge-case tests, and removing dead code to simplify execution paths.
November 2024
11 Commits • 2 Features
Nov 1, 2024November 2024 — Delivered substantial improvements to the generative testing framework and dummy data generation, and performed targeted code cleanup in the query model. These efforts increased test data realism and reliability, reduced maintenance burden, and laid groundwork for future features. Key actions included integrating the nextgen dummy data generator with deterministic RNG seeding and refined event-data distributions, reintroducing edge-case tests, and removing dead code to simplify execution paths.
Activity
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Quality Metrics
Correctness90.0%
Maintainability89.0%
Architecture82.8%
Performance78.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentCode CoverageCode DocumentationCode IntegrationCode RefactoringConfigurationData GenerationDead Code EliminationGenerative TestingPytestPythonPython DevelopmentQuery OptimizationQuery ProcessingSoftware Design
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