arcsysu/YatCC
Jun 2025 – Apr 2026
2 Months active
Languages Used
C++Python
Technical Skills
Code ParsingLLM IntegrationPython ScriptingRegular ExpressionsTestingAssembly Language
chenjr97
PROFILE
Chenjr97
Worked on the arcsysu/YatCC repository to enhance both the reliability of LLM evaluation and the correctness of LLVM IR to RV64 backend translation. Addressed output formatting and evaluation parsing by removing markdown and HTML artifacts, improving numeric parsing to handle negative and complex numbers, and expanding test coverage to reduce edge-case failures. Later, refactored the backend translation layer to improve register allocation and instruction emission, laying the groundwork for more robust RV64 support. Utilized C++, Python, and regular expressions to strengthen code parsing, error handling, and maintainability, resulting in more accurate model evaluations and a cleaner, more efficient codebase.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
25,592
Activity Months2
Your Network
21 peopleSame Organization
@mail2.sysu.edu.cn10
Shared Repositories
11
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for arcsysu/YatCC. Focused on strengthening the LLVM IR to RV64 backend with a targeted refactor to improve correctness and efficiency of code generation. Delivered a focused feature enhancement that refactors the translation layer to improve register allocation and instruction emission, laying groundwork for more robust RV64 support and future optimizations.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for arcsysu/YatCC. Focused on strengthening the LLVM IR to RV64 backend with a targeted refactor to improve correctness and efficiency of code generation. Delivered a focused feature enhancement that refactors the translation layer to improve register allocation and instruction emission, laying groundwork for more robust RV64 support and future optimizations.
April 2026
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary for arcsysu/YatCC: Focused on reliability and accuracy improvements in the LLM evaluation pipeline. The major deliverable was a bug fix for LLM Output Formatting and Evaluation Parsing, including removal of markdown/HTML artifacts, correct handling of negative numbers, and extended score calculation to parse integers and complex numbers (commit 93c7d8df9dd2357aba4bc50e9572578caccbb656). These changes reduce evaluation errors, improve metric accuracy, and enhance downstream processing. Impact: more trustworthy model evaluations, smoother user-facing outputs, and a stronger foundation for future features. Technologies/skills demonstrated include data parsing, string cleaning, numeric parsing, robust error handling, test coverage, and maintainable commit-based traceability.
June 2025
1 Commits
Jun 1, 2025June 2025 monthly summary for arcsysu/YatCC: Focused on reliability and accuracy improvements in the LLM evaluation pipeline. The major deliverable was a bug fix for LLM Output Formatting and Evaluation Parsing, including removal of markdown/HTML artifacts, correct handling of negative numbers, and extended score calculation to parse integers and complex numbers (commit 93c7d8df9dd2357aba4bc50e9572578caccbb656). These changes reduce evaluation errors, improve metric accuracy, and enhance downstream processing. Impact: more trustworthy model evaluations, smoother user-facing outputs, and a stronger foundation for future features. Technologies/skills demonstrated include data parsing, string cleaning, numeric parsing, robust error handling, test coverage, and maintainable commit-based traceability.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Assembly LanguageC++Code ParsingCompiler DesignLLM IntegrationLLVMPython ScriptingRegular ExpressionsTesting
Repositories Contributed To
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