Interview Questions Archives - Page 14 of 15

Open/Closed design principle explained with simple example

Open/Closed design principle is a software design principle which states that every module, class or function of a software should be open for extension but closed for modification. This design principle ensures any new functionality can be added via new classes which means minimum code changes required in existing classes.

Before going ahead we should know why do we need software design principle and what is software design principle.

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Single Responsibility Principle explained with simple example

by admin

Single Responsibility Principle (SRP) is a software design principle which states that every module, class or function of a software should have one and only one responsiblity/functionality to perform. As stated by Robert C.Martin, “A class should have one and only one reason to change“.

Before going ahead we should know why do we need software design principle and what is software design principle.

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Software design principle explained with simple example

by admin

Software design principle consists a set of guidelines invented by Robert C.Martin to avoid bad software design. These guidelines consists of five design principles for object oriented software development. These design principles are:

Together these are called SOLID design principles.

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Why do we need Software Design Principles

by admin

Object oriented software development often becomes victim of non-maintainable, unreliable etc in long run in most of cases. Lot of people might put bad coding skills behind that state (which is true sometime) but bad software design is the actual culprit which will make excellent coders write bad code because software design doesn’t allow them to do so.

Always remember knowing what not to do is more important than what to do !!!

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Finding number of ways to write “n” as sum of multiple smaller numbers

by Sandeep Kumar

This problem can be solved using multiple approaches but here we will solve this problem using Dynamic Programming and showcase how dynamic programming will make the execution super-fast.

We will go through with the same steps as mentioned in Dynamic Programming post to tackle this problem step by step for better understanding.

Let’s explain the problem,

given n, find the number of different ways to write “n” as the sum of 1, 3, 4

Example:

for n = 5, the answer is 6

5 = 1+1+1+1+1

= 1+1+3

= 1+3+1

= 3+1+1

= 1+4

= 4+1

Let’s apply the 3 Steps rule to tackle this problem.

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Dynamic Programming approach explained with simple example

by Sandeep Kumar

Dynamic Programming is a programming technique which is used for solving complex problems by breaking it into comparatively simpler subproblems and finding the optimal solutions of the complex problems by finding the optimal solutions of these subproblems.

Even though, the name “Dynamic Programming” might scare people but actually its kind of simple if we follow some basic techniques to approach any complex problem.

Steps to tackle a problem using Dynamic Programming approach:

1) Define smaller problems from the original complex problems.

2) Solve these smaller problems using recursion.

3) Use smaller problems results to solve the bigger complex problem.

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Heap sort explained with simple example

by Sandeep Kumar

Heap sort is one of the fastest sorting algorithm, which works on Divide and Conquer algorithm. In this sorting algorithm, all the data elements are inserted into a heap (max or min) and then removes item from the root of the heap will give the sorted(descending or ascending) data list.

Pros:
1) Faster algorithm and best performance O(nlogn).
2) Simple implementation.
3) No extra space is needed.
4) Good for cases to find biggest/smallest or top k biggest/smallest element finding kind of scenarios.

Cons:
1) Not a stable sort means position of relative identical data items is not guaranteed.

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Quick sort explained with simple example

by Sandeep Kumar

Quick sort is one of the fastest sorting algorithm, which works on Divide and Conquer algorithm. In this sorting algorithm, an element is picked as a pivot element and list is divided into sub-lists around the pivot element. These divided sub-lists finally sorted into which results into sorted initial list.

Pros:
1) Faster algorithm and best performance O(nlogn) is for worst distribution case scenario.
2) Simple implementation due to recursive nature.
3) No extra space is needed.

Cons:
1) Worst case scenario performance is O(n*n).
2) Not good for Linked list kind of sorting data where memory is not contiguous.

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Merge sort explained with simple example

by admin

Merge sort is one of the simplest sorting algorithm, which works on Divide and Conquer algorithm. In this sorting algorithm, input data is divided into two halves/sublist which then acts as input data and furthur divided into two halves so that finally every sublist contains a single element. Finally, these sublists are merged in sorted order which results into sorted data.

Pros:
1) Simple algorithm and hence efficient for small list of data.
2) Good for sorting data represented in Linked list.
3) This algorithm is stable (performance is equal in all cases) and insensitive to the distribution of initial data.

Cons:
1) Extra space needed in O(n).

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Selection sort explained with simple example

by admin

Selection sort is one of the simplest sorting algorithm, which traverse through the list, picks the minimum item from the unsorted list and place it in its place in the final sorted list.
This sorting algorithm contains (n – 1) traversal.

Pros:
1) Simple algorithm and hence efficient for small list of data.
2) No extra memory needed.
3) Good for cases when list is almost sorted (only few misplaced items).
4) Very less number of swap operation(O(n)), as in every pass only 1 swap of data is needed at max.

Cons:
1) Very slow algorithm. Not good for very large set of data.

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