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Ati Priya

PROFILE

Ati Priya

Worked on the angr/angr repository to enhance reliability and maintainability in binary analysis workflows using Python and static analysis techniques. Focused on resolving critical bugs, including improving function argument detection in calling convention analysis and expanding test coverage to reduce edge-case failures. Addressed compatibility issues by restoring standard FILE type usage, aligning the codebase with C library expectations and improving downstream integration. Implemented a robust solution for variable name collisions during semantic analysis, appending suffixes to duplicates to prevent analysis errors and improve decompiled code correctness. Emphasized test-driven development, code refactoring, and unit testing to ensure high code quality throughout.

Overall Statistics

Feature vs Bugs

0%Features

Repository Contributions

3Total
Bugs
3
Commits
3
Features
0
Lines of code
262
Activity Months3

Work History

June 2026

1 Commits

Jun 1, 2026

June 2026 monthly summary for angr/angr focusing on stabilizing semantic analysis and reducing failures in decompiled code. Implemented a robust mechanism to resolve variable name collisions during semantic analysis by appending suffixes to duplicates, preventing analysis errors and improving decompiled code correctness. This change reduces downstream debugging time and increases reliability of analysis results; aligned with lint cleanup efforts.

October 2025

1 Commits

Oct 1, 2025

October 2025 monthly summary for angr/angr focusing on compatibility and stability. This month centered on resolving a compatibility bug by restoring standard FILE type usage and aligning the codebase with standard C library expectations.

February 2025

1 Commits

Feb 1, 2025

Monthly summary for 2025-02 focusing on reliability improvements in the angr/angr calling convention analysis, specifically addressing aggressive function argument detection and expanding test coverage across diverse binary contexts. This work strengthens function-argument identification accuracy and supports downstream analysis pipelines.

Activity

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Quality Metrics

Correctness96.6%
Maintainability86.6%
Architecture86.6%
Performance83.4%
AI Usage20.0%

Skills & Technologies

Programming Languages

Python

Technical Skills

Binary AnalysisCode RefactoringPythonReverse EngineeringStatic Analysisfull stack developmentunit testing

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

angr/angr

Feb 2025 Jun 2026
3 Months active

Languages Used

Python

Technical Skills

Binary AnalysisPythonReverse EngineeringStatic AnalysisCode Refactoringfull stack development