mozilla/experimenter
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Backend DevelopmentExperimentation Platform
Rahul Sainani
PROFILE
Rahul Sainani
Developed targeted experiment functionality for the mozilla/experimenter repository by introducing large-screen device targeting within the Nimbus Experimenter platform. Implemented the ANDROID_LARGE_SCREEN_USERS_ONLY configuration in Python, updating constants.py to enable precise experiment delivery to users with large-screen Android devices. Enhanced reliability by creating dedicated unit tests in test_mobile_targeting.py to validate the is_large_device logic, ensuring accurate targeting and reducing noise in experiment results. Focused on backend development and experimentation platform best practices, with disciplined code review and git-based traceability. This work improved the platform’s ability to measure outcomes for intended audiences without addressing bug fixes during the development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
13
Activity Months1
Your Network
444 peopleSame Organization
@mozilla.com401
Work History
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05 Key outcome highlights for mozilla/experimenter: - Features delivered: Added ANDROID_LARGE_SCREEN_USERS_ONLY targeting in Nimbus Experimenter to target experiments to large-screen devices; update in constants.py; test coverage in test_mobile_targeting.py validating is_large_device. - Tests and quality: Added test_mobile_targeting.py coverage ensuring targeting logic is correct; increased confidence in device-targeted experiments. - Commits and traceability: Code committed in mozilla/experimenter (hash 5f4acd23c84f64ee3886507619695ae732b60d4a) with message feat(nimbus): add custom targeting for users with large screen devices (#12645). - Impact and value: Enables precise experiment targeting, improving user experience and reducing noise on large-screen devices; supports better measurement of experiment outcomes on intended audience. - Technologies/skills demonstrated: Python (constants.py, test_mobile_targeting.py), Nimbus Experimenter, unit testing, code review discipline, git-based change tracing.
1 Commits • 1 Features
May 1, 2025Month: 2025-05 Key outcome highlights for mozilla/experimenter: - Features delivered: Added ANDROID_LARGE_SCREEN_USERS_ONLY targeting in Nimbus Experimenter to target experiments to large-screen devices; update in constants.py; test coverage in test_mobile_targeting.py validating is_large_device. - Tests and quality: Added test_mobile_targeting.py coverage ensuring targeting logic is correct; increased confidence in device-targeted experiments. - Commits and traceability: Code committed in mozilla/experimenter (hash 5f4acd23c84f64ee3886507619695ae732b60d4a) with message feat(nimbus): add custom targeting for users with large screen devices (#12645). - Impact and value: Enables precise experiment targeting, improving user experience and reducing noise on large-screen devices; supports better measurement of experiment outcomes on intended audience. - Technologies/skills demonstrated: Python (constants.py, test_mobile_targeting.py), Nimbus Experimenter, unit testing, code review discipline, git-based change tracing.
May 2025
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentExperimentation Platform
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline