ArianitHalimi/AIN_25
Mar 2025 – Apr 2025
2 Months active
Languages Used
Python
Technical Skills
Algorithm ImplementationData StructuresProblem SolvingAlgorithm OptimizationHeuristic SearchOptimization
RinesaBislimi
PROFILE
Rinesabislimi
Rinesa Bislimi developed advanced analytics and optimization features for the ArianitHalimi/AIN_25 repository over a two-month period. She implemented an upper bound calculation for unique book scores, enabling more accurate benchmarking and capacity planning by summing scores of all distinct books across libraries. In the following month, she focused on solver optimization, introducing hill-climbing algorithms with random restarts, simulated annealing, and swap-neighbors to improve solution quality and runtime. Her work, primarily in Python, emphasized algorithm implementation, heuristic search, and software refactoring, resulting in deeper exploration of solution spaces and more scalable, maintainable code without reported defects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
352
Activity Months2
Your Network
22 peopleSame Organization
@student.uni-pr.edu6
Work History
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for ArianitHalimi/AIN_25. Focused on solver optimization and algorithmic enhancements to improve solution quality and runtime. Delivered Hill-Climbing-based optimizations with random restarts, time-based limits, simulated annealing, and swap-neighbors to replace Monte Carlo search; integrated into the main solver loop; added solution export to a file and refactored hill_climbing_with_random_restarts for better exploration and efficiency. Maintained emphasis on producing high-quality, scalable solutions with improved dictionary creation logic. No major defect fixes reported this month; stability improvements stem from algorithmic refinements and refactoring.
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for ArianitHalimi/AIN_25. Focused on solver optimization and algorithmic enhancements to improve solution quality and runtime. Delivered Hill-Climbing-based optimizations with random restarts, time-based limits, simulated annealing, and swap-neighbors to replace Monte Carlo search; integrated into the main solver loop; added solution export to a file and refactored hill_climbing_with_random_restarts for better exploration and efficiency. Maintained emphasis on producing high-quality, scalable solutions with improved dictionary creation logic. No major defect fixes reported this month; stability improvements stem from algorithmic refinements and refactoring.
April 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 completed the delivery of a new analytics capability: the upper bound calculation for a unique book score across all libraries. This feature exposes the theoretical maximum score achievable by summing scores of all distinct books, enabling better benchmarking and capacity planning. The main script was updated to call this method and print the bound for each input file, facilitating quick insights during data processing.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 completed the delivery of a new analytics capability: the upper bound calculation for a unique book score across all libraries. This feature exposes the theoretical maximum score achievable by summing scores of all distinct books, enabling better benchmarking and capacity planning. The main script was updated to call this method and print the bound for each input file, facilitating quick insights during data processing.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance65.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Algorithm ImplementationAlgorithm OptimizationData StructuresHeuristic SearchOptimizationProblem SolvingSimulated AnnealingSoftware DevelopmentSoftware Refactoring
Repositories Contributed To
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