COS301-SE-2025/Swift-Signals
Aug 2025 – Sep 2025
2 Months active
Languages Used
Python
Technical Skills
SimulationUnit TestingGenetic AlgorithmsOptimizationTraffic Management
Patterson Rainbird-Webb
PROFILE
Patterson Rainbird-webb
Over a two-month period, u1714361@tuks.co.za contributed to the COS301-SE-2025/Swift-Signals repository by developing simulation and optimization features using Python. They enhanced simulation test fidelity by enforcing a minimum speed parameter and introducing a T-junction scenario, improving both realism and test coverage. In the following month, they refactored the optimization service to use a balanced evaluation function for genetic algorithms, tuning parameters and mutation strategies to optimize traffic efficiency and safety. Their work demonstrated skills in simulation, unit testing, and optimization engineering, resulting in more maintainable code and aligning technical solutions with evolving traffic management requirements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,295
Activity Months2
Your Network
14 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Monthly summary for COS301-SE-2025/Swift-Signals focusing on feature delivery, bug fixes, and impact. This month centered on delivering the Optimization Service: Balanced Genetic Algorithm Evaluation. Key activities included refactoring the optimization service to use a single, balanced evaluation function for the genetic algorithm, and tuning parameter ranges, mutation strategies, and simulation configurations to achieve a better trade-off between traffic efficiency and safety metrics. No major bugs fixed in this period. Overall impact aligns with business goals of improved optimization quality and maintainability. Technologies demonstrated include genetic algorithms, optimization engineering, and code refactoring.
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Monthly summary for COS301-SE-2025/Swift-Signals focusing on feature delivery, bug fixes, and impact. This month centered on delivering the Optimization Service: Balanced Genetic Algorithm Evaluation. Key activities included refactoring the optimization service to use a single, balanced evaluation function for the genetic algorithm, and tuning parameter ranges, mutation strategies, and simulation configurations to achieve a better trade-off between traffic efficiency and safety metrics. No major bugs fixed in this period. Overall impact aligns with business goals of improved optimization quality and maintainability. Technologies demonstrated include genetic algorithms, optimization engineering, and code refactoring.
September 2025
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 monthly summary for COS301-SE-2025/Swift-Signals: Delivered targeted test automation and simulation configuration improvements to increase fidelity and reduce risk. Implemented a minimum speed parameter of 60 km/h across the simulation tests and added a new configuration file to enable T-junction scenario testing. These changes enhance realism, coverage, and regression reliability, enabling faster feedback and safer deployments. No major defects reported this period; focus remained on configuration and test quality. Technologies demonstrated include test configuration management, parameterized testing, and version-controlled changes.
August 2025
1 Commits • 1 Features
Aug 1, 20252025-08 monthly summary for COS301-SE-2025/Swift-Signals: Delivered targeted test automation and simulation configuration improvements to increase fidelity and reduce risk. Implemented a minimum speed parameter of 60 km/h across the simulation tests and added a new configuration file to enable T-junction scenario testing. These changes enhance realism, coverage, and regression reliability, enabling faster feedback and safer deployments. No major defects reported this period; focus remained on configuration and test quality. Technologies demonstrated include test configuration management, parameterized testing, and version-controlled changes.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Genetic AlgorithmsOptimizationSimulationTraffic ManagementUnit Testing
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