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microservice-story-manager
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Microservice Story Manager is a Python library for optimizing user story assignments in microservice architectures. It features story and microservice management, genetic algorithm optimization, and visualization tools. The library calculates metrics like coupling and cohesion, allows comparison of optimization strategies, and supports CSV data loading. It aids in microservice design decisions, helping maximize cohesion and minimize coupling in software architectures.
Microservice Story Manager is a Python library for managing user stories and microservices, optimizing story assignments, and visualizing microservice dependencies. It provides tools for organizing user stories, assigning them to microservices, and optimizing these assignments using various algorithms.
You can install the Microservice Story Manager using pip:
You can install the Microservice Story Manager using pip:
pip install microservice-story-manager
Here's a quick example of how to use the Microservice Story Manager to manage user stories and optimize their assignment to microservices:
from microservice_story_manager import UserStoryManager, MicroserviceManager, MicroserviceVisualizer, GeneticMicroserviceAssigner, FitnessFunctions, MetricsVisualizer
# Initialize managers
user_story_manager = UserStoryManager()
user_story_manager.load_stories_from_csv('path/to/your/stories.csv')
microservice_manager = MicroserviceManager(user_story_manager)
# Create microservices
for _ in range(5):
microservice_manager.create_microservice()
# Create FitnessFunctions instance
fitness_functions = FitnessFunctions(MicroserviceMetricsManager(microservice_manager))
# Choose fitness functions to compare
functions_to_compare = [
(fitness_functions.f1, "f1", True),
(fitness_functions.f2, "f2", True)
]
results = []
for func, name, minimize in functions_to_compare:
assigner = GeneticMicroserviceAssigner(user_story_manager, microservice_manager, func, minimize)
optimized_manager, fitness_value, detailed_metrics, story_assignments = assigner.optimize_assignments(generations=20, population_size=50)
results.append((name, optimized_manager, fitness_value, detailed_metrics, story_assignments))
print(f"\nResults for fitness function {name}:")
print(f"Best fitness: {fitness_value}")
print(f"Detailed metrics: {detailed_metrics}")
print("\nStory Assignments:")
for ms_id, stories in story_assignments.items():
print(f"\nMicroservice {ms_id}:")
for story in stories:
print(f" - {story}")
# Compare metrics
MetricsVisualizer.compare_metrics(results[0][3], results[1][3], results[0][0], results[1][0])
# Compare fitness values
MetricsVisualizer.compare_fitness_values([result[2] for result in results], [result[0] for result in results])
# Visualize the optimized microservices
for name, manager, _, _, _ in results:
visualizer = MicroserviceVisualizer(manager)
visualizer.plot_microservices(title=f"Optimized Microservices using {name}")
The UserStoryManager
class is used to create, store, and manage user stories. You can create user stories manually or load them from a CSV file. Here's an example of how to create a user story:
user_story_manager = UserStoryManager()
user_story_manager.load_stories_from_csv('stories.csv')
The MicroserviceManager
class is used to organize user stories into microservices. You can create microservices manually or let the manager create them for you. Here's an example of how to create microservices:
microservice_manager = MicroserviceManager(user_story_manager)
ms_id = microservice_manager.create_microservice()
microservice_manager.add_story_to_microservice(ms_id, story_id)
The GeneticMicroserviceAssigner
class is used to optimize the assignment of user stories to microservices using genetic algorithms. You can define your own fitness function to determine the optimal assignment. Here's an example of how to use the assigner:
assigner = GeneticMicroserviceAssigner(user_story_manager, microservice_manager)
assigner.optimize_assignment(fitness_function, population_size=100, generations=50)
The ParticleSwarmOptimization
class is an alternative method for optimizing story assignments. You can use it in place of the genetic algorithm assigner. Here's an example of how to use the particle swarm optimization:
pso_assigner = PSOMicroserviceAssigner(user_story_manager, microservice_manager)
best_particle = pso_assigner.optimize_assignment(population_size=50, generations=100)
pso_assigner.assign_stories_to_microservices(best_particle)
The MicroserviceVisualizer
class is used to visualize the dependencies between microservices based on user story relationships. Here's an example of how to use the visualizer:
visualizer = MicroserviceVisualizer(microservice_manager)
visualizer.plot_microservices(base_color='lightblue')
You can customize various aspects of the library:
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
FAQs
Microservice Story Manager is a Python library for optimizing user story assignments in microservice architectures. It features story and microservice management, genetic algorithm optimization, and visualization tools. The library calculates metrics like coupling and cohesion, allows comparison of optimization strategies, and supports CSV data loading. It aids in microservice design decisions, helping maximize cohesion and minimize coupling in software architectures.
We found that microservice-story-manager demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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