Identify and Refactor Repeated Code

Oct 13, 2025 by magicalceylontours.com 71 views

🎯 Objective

Refactoring the existing AIPlayer code to separate the logic for Easy, Normal, and Hard difficulty modes while maintaining clean, modular, and maintainable code is a crucial step in enhancing the overall gaming experience. This objective is achieved by restructuring the AI logic, ensuring code reusability, and preserving existing AI behavior.

📌 Scope of Work

Restructure the AI Logic

The primary goal of this scope of work is to split difficulty-specific behaviors into separate classes or modules. This will enable the development team to maintain a clean, modular, and maintainable codebase.

Ensure Code Reusability

To ensure code reusability, the shared logic will be maintained in a base AI class or utility functions. This approach will facilitate the reuse of common AI logic across difficulty levels, reducing code duplication and improving maintainability.

Preserve Existing AI Behavior

While refactoring the AI logic, it is essential to preserve the existing AI behavior. This will ensure that the game's AI continues to function as expected, without any noticeable changes to the player's experience.

📌 Tasks

1. Extract Common AI Logic into a Shared Base Class

The first task involves identifying and refactoring repeated code used across difficulty levels. This shared logic, such as valid move selection, will be moved into a base AI class. By doing so, we can ensure that common AI logic is reused across difficulty levels, reducing code duplication and improving maintainability.

Identify and Refactor Repeated Code

To extract common AI logic into a shared base class, we need to identify repeated code used across difficulty levels. This can be achieved by:

Conducting a thorough code review to identify duplicated code
Refactoring the duplicated code into a separate module or function
Moving the shared logic into a base AI class

Move Shared Logic into a Base AI Class

Once the repeated code has been identified and refactored, we can move the shared logic into a base AI class. This will ensure that common AI logic is reused across difficulty levels, reducing code duplication and improving maintainability.

2. Create Separate AI Difficulty Classes

The second task involves refactoring existing AI behaviors into EasyAIPlayer, NormalAIPlayer, and HardAIPlayer classes. Each subclass will only contain difficulty-specific logic, while inheriting from the base AI class.

Refactor Existing AI Behaviors

To create separate AI difficulty classes, we need to refactor existing AI behaviors into separate classes. This can be achieved by:

Identifying difficulty-specific logic in the existing AI code
Refactoring the difficulty-specific logic into separate classes
Ensuring each subclass only contains difficulty-specific logic

Ensure Each Subclass Only Contains Difficulty-Specific Logic

Once the existing AI behaviors have been refactored, we need to ensure that each subclass only contains difficulty-specific logic. This can be achieved by:

Reviewing the code to ensure that each subclass only contains difficulty-specific logic
Removing any shared logic from the subclasses
Ensuring that each subclass only inherits from the base AI class

3. Ensure Difficulty is Configurable

The third task involves modifying game initialization to allow easy selection of AI difficulty. This will ensure that the AI behaves exactly as before but with a cleaner, modular structure.

Modify Game Initialization

To ensure difficulty is configurable, we need to modify game initialization to allow easy selection of AI difficulty. This can be achieved by:

Adding a configuration setting to select AI difficulty
Modifying the game initialization to use the selected AI difficulty
Ensuring that the AI behaves exactly as before but with a cleaner, modular structure

4. Remove Redundant or Hardcoded Logic

The fourth task involves identifying and eliminating any duplicated code across difficulty implementations. This will ensure that AI logic is structured for easy future modifications.

Identify and Eliminate Duplicated Code

To remove redundant or hardcoded logic, we need to identify and eliminate any duplicated code across difficulty implementations. This can be achieved by:

Conducting a thorough code review to identify duplicated code
Refactoring the duplicated code into a separate module or function
Eliminating any duplicated code

Ensure AI Logic is Structured for Easy Future Modifications

Once the duplicated code has been eliminated, we need to ensure that AI logic is structured for easy future modifications. This can be achieved by:

Reviewing the code to ensure that AI logic is structured for easy future modifications
Ensuring that AI logic is modular and maintainable
Ensuring that AI logic is easy to extend and modify

✅ Acceptance Criteria

The AI Logic is Modularized

The AI logic is modularized into separate difficulty-based classes.

Code Duplication is Minimized

Code duplication is minimized by using inheritance or shared functions.

AI Behavior Remains Functionally Unchanged

AI behavior remains functionally unchanged after the refactor.

Game Difficulty Can be Selected Dynamically

Game difficulty can be selected dynamically via a configuration setting.

📌 Dependencies

This Task Does Not Modify AI Behavior

This task does not modify AI behavior, only its structure.

Subtasks for Implementing and Refining AI Logic for Each Difficulty Level Will be Handled Separately

🎯 Objective

Refactoring the existing AIPlayer code to separate the logic for Easy, Normal, and Hard difficulty modes while maintaining clean, modular, and maintainable code is a crucial step in enhancing the overall gaming experience. This Q&A article provides answers to frequently asked questions related to this refactor.

Q: What is the main goal of refactoring the AIPlayer code?

A: The main goal of refactoring the AIPlayer code is to separate the logic for Easy, Normal, and Hard difficulty modes while maintaining clean, modular, and maintainable code.

Q: Why is it necessary to refactor the AIPlayer code?

A: Refactoring the AIPlayer code is necessary to ensure that the game's AI continues to function as expected, without any noticeable changes to the player's experience. This will also enable the development team to maintain a clean, modular, and maintainable codebase.

Q: What are the benefits of refactoring the AIPlayer code?

A: The benefits of refactoring the AIPlayer code include:

Improved maintainability
Reduced code duplication
Improved scalability
Enhanced flexibility

Q: What are the tasks involved in refactoring the AIPlayer code?

A: The tasks involved in refactoring the AIPlayer code include:

Extracting common AI logic into a shared base class
Creating separate AI difficulty classes
Ensuring difficulty is configurable
Removing redundant or hardcoded logic

Q: What is the importance of extracting common AI logic into a shared base class?

A: Extracting common AI logic into a shared base class is essential to ensure that common AI logic is reused across difficulty levels, reducing code duplication and improving maintainability.

Q: How can we ensure that each subclass only contains difficulty-specific logic?

A: We can ensure that each subclass only contains difficulty-specific logic by:

Reviewing the code to ensure that each subclass only contains difficulty-specific logic
Removing any shared logic from the subclasses
Ensuring that each subclass only inherits from the base AI class

Q: What is the significance of ensuring difficulty is configurable?

A: Ensuring difficulty is configurable is essential to allow easy selection of AI difficulty, ensuring that the AI behaves exactly as before but with a cleaner, modular structure.

Q: How can we ensure that AI logic is structured for easy future modifications?

A: We can ensure that AI logic is structured for easy future modifications by:

Reviewing the code to ensure that AI logic is structured for easy future modifications
Ensuring that AI logic is modular and maintainable
Ensuring that AI logic is easy to extend and modify

Q: What are the acceptance criteria for this refactor?

A: The acceptance criteria for this refactor include:

The AI logic is modularized into separate difficulty-based classes
Code duplication is minimized by using inheritance or shared functions
AI behavior remains functionally unchanged after the refactor
Game difficulty can be selected dynamically via a configuration setting

Q: What are the dependencies for this refactor?

A: The dependencies for this refactor include:

This task does not modify AI behavior, only its structure
Subtasks for implementing and refining AI logic for each difficulty level will be handled separately

Q: What are the benefits of using a modular and maintainable codebase?

A: The benefits of using a modular and maintainable codebase include:

Improved scalability
Enhanced flexibility
Reduced code duplication
Improved maintainability

Q: How can we ensure that the AI logic is modular and maintainable?

A: We can ensure that the AI logic is modular and maintainable by:

Reviewing the code to ensure that AI logic is modular and maintainable
Ensuring that AI logic is easy to extend and modify
Ensuring that AI logic is structured for easy future modifications