Description: Behavioral Strategy is a design pattern that defines a family of algorithms, encapsulating each one and making them interchangeable. This approach allows developers to select the most suitable algorithm for a specific task at runtime, without needing to modify the code that uses these algorithms. Behavioral Strategy focuses on separating business logic from the algorithms used to perform specific tasks, promoting code reuse and flexibility in software design. By encapsulating algorithms, it facilitates the addition of new behaviors without altering existing code, which is crucial in agile development environments. This pattern is particularly useful in situations where an object’s behavior needs to change based on different conditions or states, allowing the system to adapt to new requirements without complications. In summary, Behavioral Strategy is a powerful tool in behavior-driven development that helps create more modular and maintainable systems.
History: Behavioral Strategy, as a design pattern, was popularized by the book ‘Design Patterns: Elements of Reusable Object-Oriented Software’ by Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, published in 1994. This book, known as the ‘Gang of Four’ (GoF), compiled and systematized design patterns that had been used in object-oriented programming, establishing a common language for developers. Since its publication, Behavioral Strategy has evolved and adapted to various technologies and programming languages, becoming a standard in software development.
Uses: Behavioral Strategy is used in various software applications where flexibility and adaptability in object behavior are required. It is common in systems that need to change their behavior based on user input or system state. For example, it can be applied in applications to define different behaviors based on specific criteria or conditions.
Examples: A practical example of Behavioral Strategy is the use of different sorting algorithms in an application. Depending on the size and nature of the data, one can choose between algorithms like QuickSort, MergeSort, or BubbleSort, encapsulating each in its own class. Another example can be found in messaging applications, where different message sending strategies (such as SMS, email, or push notifications) can be implemented based on user preference.
