NCATComp410/comp410_spring_2025
Feb 2025 – Feb 2025
1 Month active
Languages Used
Python
Technical Skills
PythonRegular ExpressionsTest-Driven DevelopmentText AnalysisUnit Testing
Quentin Johnson
PROFILE
Quentin Johnson
Developed a context-sensitive US bank number detection feature for the NCATComp410/comp410_spring_2025 repository, enhancing the accuracy of PII scanning by incorporating contextual cues and robust validation. Leveraged Python and regular expressions to refine detection logic, focusing on increasing confidence scores through analysis of both valid and invalid inputs, including short strings and preceding words. Employed test-driven development and comprehensive unit testing to ensure reliability and reduce edge-case misclassifications. The work improved data privacy safeguards by minimizing false positives and negatives, demonstrating a methodical approach to text analysis and validation within a production-oriented codebase over the course of the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
36
Activity Months1
Your Network
15 peopleSame Organization
@aggies.ncat.edu7
Work History
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 (Month: 2025-02) performance summary for NCATComp410/comp410_spring_2025. Delivered reinforced detection of US bank numbers in PII scanning with context-sensitive recognition and robust test coverage. This work includes context-aware recognition to increase confidence scores, validated against positive and negative inputs, including short strings and contextual preceding words. No separate critical bugs reported; robustness enhancements reduced edge-case misclassification risk and improved overall data privacy safeguards.
3 Commits • 1 Features
Feb 1, 2025February 2025 (Month: 2025-02) performance summary for NCATComp410/comp410_spring_2025. Delivered reinforced detection of US bank numbers in PII scanning with context-sensitive recognition and robust test coverage. This work includes context-aware recognition to increase confidence scores, validated against positive and negative inputs, including short strings and contextual preceding words. No separate critical bugs reported; robustness enhancements reduced edge-case misclassification risk and improved overall data privacy safeguards.
February 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture66.6%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PythonRegular ExpressionsTest-Driven DevelopmentText AnalysisUnit Testing
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