google/perfetto
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++PythonSQLTypeScriptccprotopyts
Technical Skills
DebuggingExperimental Feature ImplementationFeature TogglingFrontend DevelopmentPerformance AnalysisProtocol Buffers
adyabr
PROFILE
Adyabr
Adyabr developed experimental jank classification and visualization enhancements for the FrameTimeline system in the google/perfetto repository. Their work introduced new data fields and a user-controlled toggle to switch between traditional and experimental jank classification, enabling side-by-side visual comparison of results. By adding the JANK_DISPLAY_NOT_ON type, Adyabr improved the accuracy of performance reporting during inactive display states. The implementation leveraged TypeScript and C++ for UI and backend changes, with Protocol Buffers used for data modeling. This feature group provided a flexible experimentation pathway for users, supporting earlier detection of performance regressions and more granular analysis of system performance issues.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
331
Activity Months1
Your Network
4376 people
Work History
October 2025
4 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 (google/perfetto): Key features delivered: - Experimental Jank Classification and Visualization Enhancements for FrameTimeline: delivered a consolidated feature group enabling experimental jank measurement fields, a user toggle to switch between traditional and experimental classification, an experimental side-by-side comparison track, and a new jank type JANK_DISPLAY_NOT_ON to capture jank when the display is not active. Major bugs fixed: - No critical bugs reported this month. Efforts focused on feature rollout and data consistency improvements, including robust handling for display-off scenarios. Overall impact and accomplishments: - Strengthened frame timing analytics with richer data fields and flexible classification, enabling earlier detection of performance regressions and improved diagnosis of jank. - Provided users with an adjustable experimentation pathway (feature toggle) and visual aids (side-by-side track) to compare classification approaches. - Improved reporting granularity for non-active display states, enhancing accuracy of performance measurements during idle periods. Technologies/skills demonstrated: - Data modeling and instrumentation for FrameTimeline in Perfetto; experimentation via feature toggles; UI/visualization enhancements; commit-driven development; cross-functional collaboration via incremental commits. Commits (representative): - c63922cabbde52bacdd51a17ea6cc472ca5aceb3: Add a few additional fields to Frametimeline, currently in experimental mode (#3159) - f1469e753c78b52cbb4fe1abb82141ac82903bc0: Add a settings toggle for experimental jank classification (#3180) - 69bd8a4b80aca14a9f07b5443572bb34150e496c: Show an additional track for experimental jank classification for a side-by-side comparison (#3184) - 9e88d7f37407ab04dc3e158d6b8673a06014430f: Add a new jank type for when the display is not ON (#3356)
4 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10 (google/perfetto): Key features delivered: - Experimental Jank Classification and Visualization Enhancements for FrameTimeline: delivered a consolidated feature group enabling experimental jank measurement fields, a user toggle to switch between traditional and experimental classification, an experimental side-by-side comparison track, and a new jank type JANK_DISPLAY_NOT_ON to capture jank when the display is not active. Major bugs fixed: - No critical bugs reported this month. Efforts focused on feature rollout and data consistency improvements, including robust handling for display-off scenarios. Overall impact and accomplishments: - Strengthened frame timing analytics with richer data fields and flexible classification, enabling earlier detection of performance regressions and improved diagnosis of jank. - Provided users with an adjustable experimentation pathway (feature toggle) and visual aids (side-by-side track) to compare classification approaches. - Improved reporting granularity for non-active display states, enhancing accuracy of performance measurements during idle periods. Technologies/skills demonstrated: - Data modeling and instrumentation for FrameTimeline in Perfetto; experimentation via feature toggles; UI/visualization enhancements; commit-driven development; cross-functional collaboration via incremental commits. Commits (representative): - c63922cabbde52bacdd51a17ea6cc472ca5aceb3: Add a few additional fields to Frametimeline, currently in experimental mode (#3159) - f1469e753c78b52cbb4fe1abb82141ac82903bc0: Add a settings toggle for experimental jank classification (#3180) - 69bd8a4b80aca14a9f07b5443572bb34150e496c: Show an additional track for experimental jank classification for a side-by-side comparison (#3184) - 9e88d7f37407ab04dc3e158d6b8673a06014430f: Add a new jank type for when the display is not ON (#3356)
October 2025
Activity
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Quality Metrics
Correctness87.6%
Maintainability87.6%
Architecture87.6%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonSQLTypeScriptccprotopyts
Technical Skills
DebuggingExperimental Feature ImplementationFeature TogglingFrontend DevelopmentPerformance AnalysisProtocol BuffersSystem TracingTrace ProcessingUI Developmentdata modelingdebuggingperformance analysistrace processing
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