How to Reduce Code Duplication in Scala?

For this definition, duplicate code refers to a sequence of source code that appears more than once in a program, whether inside the same program or across various programs owned or maintained by the same company. For a variety of reasons, duplicate code is typically seen as bad. A minimal requirement is often applied to the amount of code that must exist in a sequence in order for it to be regarded as duplicate rather than coincidentally similar, and this is known as the minimum requirement. Code clones, or simply clones, are sequences of duplicate code that may be found in source code. The automated technique of detecting duplication in source code is referred to as clone detection. An instance of this is when a piece of code occurs more than once in a code base. It occurs for a variety of causes, including Someone needing to reuse a method in a separate class, and copy-paste proved to be the most efficient approach. In addition, code that incorporates duplicate functionality is more difficult to sustain since it is just greater in length. The risk of one copy of code being updated without first checking for the existence of other copies is that the code will be modified without first determining whether it is needed to be updated.

Scala reduces the function that is used to decrease the collection data structure in the Scala programming language. This method may be used for both mutable and immutable collection data structures, depending on the data structure. Mutable objects are ones whose values change on a regular basis, while immutable objects are those to which one sets values that cannot be changed. Reduce function may be used for a variety of data types, including a map, sequence, set, and list, among others. This method returns a single value from the data structure that contains a collection of values. In the reduction function, all of the values from the collection data structure are merged together and returned as a single value. The following is the Scala reduce syntax:

def reduce[A1 >: A](op: (A1, A1) ⇒ A1): A1

According to the Scala documentation, this is the syntax to use when defining a reduced function in the language. It will eventually return a single value from the collection of items. Now, let's look at a practice example when def is declaring it into the program:

val result: Int = collection_name.reduce((a1, b1) => a1 + b1)

collection name.reduce((a1, b1)) returns Int, which is the outcome of the reduced operation.

Explanation

As seen in the preceding syntax, we have created a collection, which is then followed by a reduced function, which accepts two arguments on which we may conduct our action. At the end of the process, we will have a single value as an output. The following are some ideas for reducing code duplication in Scala: In the collection data structure, the Reduce function lowers the number of items. Internally, it performs a binary operation on all of the accessible components, resulting in a single element consisting of all of them. We can see how it operates on the inside. Consider the following scenario: we have a single list with such many entries as 3, 7, 9, 2, 1, and 5. After we have added all of the numbers from the list, we will apply the reduction function to it in order to produce a single number from it.

1. First, it will take the first element 3, and then it will perform a binary operation on the first and second numbers, merging them to get the third element. 2.
2. f(3, 7) -> 10: This will generate the third number by using the binary operation on the first three numbers.
3. When the following two elements are added, the result will be 19. 3. f(10, 9) -> 19, not the result will be 19.
4. f(19, 2) -> 21 After performing the binary operation, the sum of the following two items is equal to the number 21.
5. f(21, 1) = 22; the sum of the following two values is 22.

In this case, the next two components are 22 and 5; hence, the merge value would be 26; and this is the last element in the list. As a result, the final value is 26. It will be created as a result of the operation. The reduction function iterates through the objects in the list(=>) by using the Anonymous function. The operation on the reduce function is both associative and commutative.

There are three easy strategies to lessen our carbon footprint

Making Use of a Test Fixture: A test fixture is made up of objects and other artifacts (such as files, sockets, database connections, and so on) that are used by tests to perform their tasks. When several tests need the use of the same fixtures, it is critical to avoid duplicating the fixture code across all of the tests in question. The bigger the amount of code duplication present in our tests, the greater the amount of time it will take to restructure the real production code. The test fixture may be used to set up the whole environment that will be needed by the test case.

1. Create the initial state of repositories, services, the actor system, database connections, and other such things.
2. Create the test data for analysis.
3. Destroy the application, disconnect the database, or do additional steps after the test. 

Using custom traits: Another method is to use custom traits to describe the elements of the application that are necessary. It may be particularly beneficial in scenarios when your application is specified in trait modules, or when we wish to specify different elements of the fixture in distinct traits and mix them suitably for various test cases, like in the following examples. There are some substantial disadvantages to using this strategy as well. At the end of the day, we may be confronted with a complicated trait structure for the fixture that is tough to reason about. Because the services supplied by the specified fixture are not clearly described in the test — there are just a few values in the parent trait — we must understand what services are provided by the fixture in question. There is no straightforward method of parameterizing the test cases. Additionally, we may specify extra abstract values for a trait with the risk of receiving a random Null Pointer Exception due to faulty val initialization in complicated class structures generated by the Scala compiler. Lastly, there is no straightforward method of implementing the tear down in these types of fixtures.

Using Functions: We can also use functions to create our test fixtures if we want to simplify things. This technique is quite similar to the Fixture function from Scala Test in terms of the body of a fixture function however, there are some minor variations and significant benefits to using this approach. We may easily share fixture functions across various test suites by simply dragging and dropping them. we may also utilize many fixtures in a single suite if we want to save space. This method is quite versatile since it is not constrained by base class rules. The main disadvantage of this strategy, in comparison to the other two options, is the inability to combine numerous fixtures. In the absence of a straightforward mechanism such as trait mix-in, we must either utilize the other fixtures directly or devise a new method of integrating them.

Using the Reduce function in Scala to find the Addition

By using a binary operation, we are able to calculate the total of all of the components included inside the collection data structure.

Example 1:  

object Main extends App 
{

// creating collection
val list1 = List(2000 , 4000, 1000, 300, 10000, 5000)
println("list before ::")
println(list1)

// applying reduce function
val result = list1.reduce((x1, y1) => x1 + y1)
println("list after ::")
println(result)

}

Output:

list before::
List(2000, 4000, 1000, 300, 10000, 5000)
list after::
22300

Using the Reduce function in Scala to find the Division

By using a binary operation, we are able to determine the division of all of the items included in the collection data structure.

Example 2:

object Main extends App
{

  // creating collection
  val list1 = List(2000 , 4000, 1000, 300, 10000, 5000)
  println("list before ::")
  println(list1)

  // applying reduce function
  val result = list1.reduce((x1, y1) => x1 / y1)
  println("list after ::")
  println(result)

}

Output:

list before:
List(200, 4000, 1000, 300, 10000, 5000)
list after:
0