Python *args and**kwargs. Could You Realize a Superior Way of Defining Your Functions?

Python *args and**kwargs. Could You Realize a Superior Way of Defining Your Functions?

Python Args Kwargs

How to use *args and **kwargs in Python

In Python, you define a function to make a code that performs this operation. To do this, you need to call a function with a value, which is called a function argument in Python.

We define a function that takes 2 arguments. It is a function for the addition of 2 numbers.

 Example of the function to add 2 numbers:

def add(x1,x2): 
    print("sum:", x1+x2)
 
add(14,16) 
 
#Output:
#sum: 30

As you can see in this program add() function needs to take 2 arguments x1 and x2. We get a sum of two numbers as a result when we pass two values while calling the function. But if we need to get a sum of 4 numbers. Let’s see what happens when we pass 4 numbers instead of 2 in the add() function. 

def add(x1,x2): 
print("sum:",x1+x2) 

add(9,11,13,15)

When we run the above program, we get output like these:

#Output:
#TypeError: add() takes 2 positional arguments but 4 were given

In conclusion, we can say that you can’t pass more arguments in a function, because it has a limited quantity of arguments. But what if you need to sum up a different number of arguments? It would be good if we can create a function, where the number of arguments passed is only determined at runtime. 

Here come *args and **kwargs. Args and kwargs in Python allow you to pass a variable number of arguments to a function. For, this you only need to use them as arguments in a function. At first, you need to understand the difference between *args and **kwargs. *args in Python are non keyword arguments and **kwargs mean keyword arguments in Python.

Keyword and non-keyword arguments

Let’s start from the basics. Arguments to built-in functions (or methods) can be passed by keyword parameter in Python, and the language has the ability to collect both positional and/or keyword arguments in a single call.

Positional or, in our case, non-keyword arguments are moved into the function by the order in which the parameters were recorded during the interpretation of the function. Therefore, it means that the order of arguments and parameters in Python is strongly important as the values passed to these functions are assigned to the analogous parameters based on their position. Actually, they are matched from left to right.

Example of using non-keyword arguments:

def example(name, date):
print(name + "`s birthday is on", date)

example("David", "12/01/2001")

#Output:
#David`s birthday is on 12/01/2001

Here are name and date are the arguments that are strongly located in the order 1.name 2.date. So when we use the function we need to type it in the same form.

Keyword arguments (or named arguments) are values that, when preceded by a function, are identified by specific parameter names. The key argument is passed by a parameter and the operator = that assigns it. Keyword arguments can be compared to dictionaries in that they map a value to a keyword. Therefore, the order in which these arguments are passed to the caller does not matter, as they correspond to the argument name.

def example(name, date):
print(name + "`s birthday is on", date)

example(date = "12/01/2001", name = "David")

#Output:
#David`s birthday is in 12/01/2001

And as mentioned already, the order that we provide keyword arguments doesn’t really matter: here name and date are not located in the same order as defined in the function. We used the name of an argument and made it equal to the content needed for it.

Take note that we can even make a function where non-keyword arguments are with keyword arguments, but take note again that if you provide a keyword argument before non-keyword, you can get SyntaxError

Let’s see two examples:

def example(name, date, location):
print(name, "is making birthday on", date, "in the", location)

example("David", location = "school", date="12/01/2001")

#Output:
#David is making birthday on 12/01/2001 in the school

We used different types of arguments. Here the argument ‘name’ became a positional argument and ‘location’ and ‘date’ – keyword argument. The next example will have a mistake:

def example(name, date, location):
print(name, "is making birthday on", date, "in the", location)

example(location = "school", "David", date="12/01/2001")

#Output:
#SyntaxError: positional argument follows keyword argument

And the mistake is that we have put the positional argument after the keyword argument. Again ‘name’ is the non-keyword argument and the other two are keyword arguments.

Python *args

In the example above the number of arguments is established, so we can not decide ourselves how many arguments to pass. Python arg can help us in this situation by passing the variable number of non keyword arguments to function. In the parentheses we need to use an asterisk * before the name of the keyword parameter Python to pass a variable quantity of arguments. Arguments are passed as a tuple, and those passed arguments create a tuple in a function with the same name as the parameter, excluding *.

Python argument passing example:

def multiply(x): 
print(x*x)

multiply(13)

#Output:
#169

And the example using *arg Python:

def example(*args): 
print(args)

example(13, 14, 16)

#Output:
#(13, 14, 16)

So actually we can improve our function to sum 4 numbers, 2 or another one you want. Take note that name *args here is just the name and we can use another one that we prefer.

 Example of using *args Python named as *numbers:

def add(*numbers):
sum = 0

for n in numbers:
sum = sum + n

print("sum:",sum)

add(13,14)
add(13,15,17,19)

#Output:
#sum: 27
#sum: 64

Here we used *numbers as a parameter so we are allowed to pass a variable quantity of argument lists to add() function. As you can see, we have created a loop that adds the passed argument inside the function and prints the result. *numbers takes all parameters that we have provided in the input and then packs them into a single iterable object named args. So we have passed 2 different tuples with variable lengths.

Using the Python args Variable in definitions of the Functions

Undoubtedly, there can be a lot of ways to pass varying lengths of arguments to the function. But using argss is one of the simplest. We can simply pass the whole list or a set of all arguments to our function. Here is an example of a harder way how to do it:

def sumOfElements(numbers_list):
example_list = 0

for n in numbers_list:
example_list += n
return example_list

list_of_numbers = [14, 15.5, 145]
print("sum of elements:", sumOfElements(list_of_numbers))
#Output:
#sum of elements: 174.5

So here we created the list and passed it into the function. This way can be useful when we already know what integers we want to use. But what to do when we want to implement elements into the function and we do not know what they are? Here is where our *args comes into our function.

 Args python example we have already seen before:

def sumOfElements(*numbers):
example_list = 0

for n in numbers:
example_list += n

print("sum of elements:",example_list)

sumOfElements(14, 15.5, 145)

#Output:
#sum of elements: 174.5

And here we see that all elements are added after the function. So it is more easier and more convenient for us to use *args in the function.

Also, we can make the opposite usage of asterisk operators, where the asterisk symbol will not be used when we define the function, but when we call it:

def personal_name(first, second, third):
print("Name:", first)
print("Middle name:", second)
print("Surname:", third)

args = ("John", "Fitzgerald", "Kennedy")
personal_name(*args)

#Output:
# Name: John
# Middle name: Fitzgerald
# Surname: Kennedy

def personal_name(first, second, third):
print("Name:", first)
print("Middle name:", second)
print("Surname:", third)

args = ("John", "Fitzgerald", "Kennedy")
personal_name(*args)

#Output:
# Name: John
# Middle name: Fitzgerald
# Surname: Kennedy

Here we created three parameters: first, second, and third. We created a variable args that will be our tuple. Asterisk syntax helped us to pass the variable into the function. 

Also, we can combine already-named parameters with the arg in Python. Here is an example:

Python kwargs tutorial

**kwargs are useful for passing a variable length of arguments to a Python function. In the parentheses we use two asterisks ** before the name of the parameter to denote the type of argument. Python keyword arguments are passed as a dictionary, as we know now *args are passed as a tuple, and those passed arguments create the dictionary in the function with the same name as the parameter, excluding **. Similar to *args example, let’s try to make an example of **kwargs:

def example(**kwargs): 
print(kwargs)

example(13, 14, 16)

#Output:
#TypeError: example() takes 0 positional arguments but 3 were given

And we have got a TypeError. The next example of using kwargs Python will explain why it happened:

def example(**kwargs): 
print(kwargs)

example(first = 13, second = 14, third = 16)

#Output:
#{'first': 13, 'second': 14, 'third': 16}

Here we can see, that the main difference between *args and **kwargs is the difference of the types. When using *args we create a tuple, **kwargs only receive dictionaries. Don’t forget that name don’t need to be **kwargs again. It can be another one that we prefer, but be careful with asterisks!

def example(**kwargs):
for first, second in kwargs.items():
print("{0} is {1}".format(first, second))

example(Name="Kevin")

#Output:
#Name is Kevin

Here we used the .format() string method to specify values and insert them inside the string’s placeholder. kwarg helps us to work with the Name variable, so we can take the content from it and push it in the curly brackets of the .format(first, second).

Using the Python kwargs Variable in definitions of Functions

So now we have understood what are **kwargs. They work same as *args but instead of non-keyword arguments it accepts keyword ones. Let’s make an example so we can see why **kwargs are useful enough:

def example(**sentence):
example_list = ""
for n in sentence.values():
example_list += n
print(sentence)

example(a="Good ", b="morning, ", c="KoderShop", d="!")

#Output:
#{'a': 'Good ', 'b': 'morning, ', 'c': 'KoderShop', 'd': '!'}

Making some formatting we can make it more readable:

def example(**sentence):
example_list = ""
for n in sentence.values():
example_list += n
return example_list

print(example(a="Good ", b="morning, ", c="KoderShop", d="!"))

#Output:
#Good morning, KoderShop!

Here we used print features to make the code look more accurate and now we can actually see how good **kwargs is. Note that in the example above, the iterated object is a standart dict. If you are looping through a kwargs dictionary and want to return its values, as in the example above, you should use .values().

In another situation, if you want to take only keys in the dictionary, you need to remove .values(), like in the example below:

def example(**sentence):
example_list = ""
for n in sentence:
example_list += n
return example_list

print(example(a="Good ", b="morning, ", c="KoderShop", d="!"))

#Output:
#abcd

When can we use them?

It really depends on the requirements. The most common use case of args kwargs Python is to create function decorators. In addition, it can also be used to repair monkeys. Let’s say you have a class with a get_info function that calls an API and returns response data. If we want to test this, we can replace the API call with test data. Sounds difficult, but undoubtedly I think Python args kwargs can have a lot of ways to make them useful.

Function order of the arguments

When we create a function that takes a changeable number of both positional and named arguments, the order counts. Take a note, or learn by heart the set order when ordering args in a function and undoubtedly function call. The *args like non-default arguments have to precede the **kwargs that are like default arguments. Let’s see the correct order of parameters:

First – Standard arguments;

Second – *args;

And the last – **kwargs.

 

The next question is: what happens if we define a function with non-correct order of parameters? Here is an example:

def example(**sentence, *args, x):
print("Is it right?")

#Output:
#def example(**sentence, *args, x):
# ^
#SyntaxError: invalid syntax

Here we can see that **kwargs precedes *args in a function definition. If you try to run this example, you immediately get an error message from the interpreter and it does not matter what you have written next in the code.

The correct ones will look like that:

def example(*numbers, **keys):
words_list = ""
sum_of = 0
for n in numbers:
sum_of += n
for m in keys:
words_list += m + ' '
print('Sum of numbers:', sum_of, '\nWords from keys:', words_list)

example(1, 2, 12.5, Watch='first', how='second', I='third', can='fourth')

#Output:
#Sum of numbers: 15.5 
#Words from keys: Watch how I can

And here we used 2 arguments *args and **kwargs to complete our sum_of and words_list.

Unpacking operators in Python

We use *args and **kwargs to define Python functions that accept different numbers of input arguments. Let’s understand a bit more about these operators.

These single and double operators are in Python from the Python 2.

Single and double star unpacking operators were introduced in Python 2. As people say, they have become more powerful only in Python 3 thanks to PEP 448, which was created in June 2013.

This PEP offers extended use of the * iterative unpacking operator and the ** dictionary unpacking operators to allow us to use it in more positions under additional circumstances, such as function calls, in generator understandings and expressions, and in mappings. So what are these “unpacking operators”?

In short, unpacking operators are operators that unpack values ​​from iterated objects in Python. The single-star * operator can be used for any iterator that Python provides, while the double-star ** operator can only be used for dictionaries.

Unpacking lists

Let’s see some examples of lists:

example_list = ['Hello,', 'KoderShop', '!']
print(example_list)

#Output:
#['Hello,', 'KoderShop', '!']

Here we see how the standard output of the list works. We have square brackets, commas and our values. We do not want that. Now we gonna use unpacking operator *. What will the output be?

example_list = ['Hello,', 'KoderShop!']
print(*example_list)

#Output:
#Hello, KoderShop!

So here the asterisk operator talks with print and makes it unpack the list first. We do not have these annoying brackets and commas and can see only the content of the list. And also we have changed arguments so the content in the output will look more clear.

It is one explanation of what asterisks can do. They make print() in-built functions take these two separate arguments as the input.

Calling functions

This asterisk method we can use to call our functions. The only rule is that we should make the iterable we unpack to have the same number of arguments if we want our function to have a specific number of arguments.

def example(first, second, third):
print(first * second * third)

example_list = [1, 2, 3]
example(*example_list)

#Output:
#6

So we have here the *example_list which means that we have unpacked our list and used only content from it to make the multiplication we need. Try yourself, what will happen without the asterisk…

def example(first, second, third):
print(first * second * third)

example_list = [1, 2, 3]
example(example_list)

#Output:
#TypeError: example() missing 2 required positional arguments: 'second' and 'third'

Before the example of an error, we had 3 elements in our example_list that meet up the required arguments in example(). 

Example, where the function requires 5 arguments, but we make only 3:

def example(first, second, third):
print(first * second * third)

example_list = [1, 2, 3, 4, 5]
example(*example_list)

#Output:
#TypeError: example() takes 3 positional arguments but 5 were given

Here we see how the Python interpreter is unable to run it because example() expects 3 items, but the unpacking operator gets 5 items from our list. The next part of the code will not run.

Several unpacking operators

To unpack the list we use the * unpacking operator so we can pass arguments to a function. It means that we pass every single argument by itself. And again that means using multiple asterisk operators is possible and we get content from several lists. That is all we pass to a single function.

Here is one of the examples:

def sentence(*args):
temp = ''
for n in args:
temp += n
print(temp)

words1 = ['— Hello, ' 'how ', 'are ', 'you?\n']
words2 = ['― I ', 'am ', 'fine, ', 'thanks. ', 'And ', 'you?\n']
words3 = ['― Me ', 'too.']
sentence(*words1, *words2, *words3)

#Output:
# — Hello, how are you?
# ― I am fine, thanks. And you?
# ― Me too.

Running this example, we see that our lists of words were successfully unpacked. Every word was passed to the function sentence() so we can see the speech. In the end of every item, we have added the space so it could look like the normal sentence but not the array of symbols. Also we used the ‘\n’ newline character at the end of the last item in the list.

Split and merge using unpacking operators

Here is another way to use the unpacking operator. For instance, we have a list and our wish is to have it split into three parts, where the last value of the list will be at the end of the output, first value will be at the start, and another value will be between.

example_list = [1, 26, 3.3, 4, 5.3, 6]
first, *second, third = example_list

print(first)
print(second)
print(third)

#Output:
#1
#[26, 3.3, 4, 5.3]
#6

Here we see 6 items in the example_list. We create 3 variables. First(example_list[0]) must be at the start, *second at the middle, and third(example_list[5]) at the end. The new list *second is a created list of all other elements. In the output section we see how to print() shows us our three variables have the values we expected.

 

Also using unpacking operators * we can split items of iterable objects. For example, we decided to merge two lists, so it could be useful:

first = 1
second = [26, 3.3, 4, 5.3]
third = 6

example_list = [first,*second,third]
print(example_list)

#Output:
#[1, 26, 3.3, 4, 5.3, 6]

As it worked before it worked also here. *second took all the elements from the second list inside and added them into the example_list. Integers from the first and third were added using standard in-built syntax.

 

Asterisk operators can also even merge two different dictionaries. We need to use now double-asterisk operator **:

first = {'Ab':3, 'Cd':2}
second = {'Ef':1}

example_list = {**first,**second}
print(example_list)

#Output:
#{'Ab': 3, 'Cd': 2, 'Ef': 1}

So now we created three dictionaries, where the third one example_list is the merged list from two others. Firstly, we can see that now we use curly brackets, so we know that they are dictionaries, and also double-asterisk operators, to show that we are working with dictionaries.

 Another feature of unpacking operators is unpacking strings. Remember now that * operator is used on any iterable object. Let’s see some examples of string:

example = [*"KoderShop is the best!"]
print(example)

#Output:
#['K', 'o', 'd', 'e', 'r', 'S', 'h', 'o', 'p', ' ', 'i', 's', ' ', 't', 'h', 'e', ' ', 'b', 'e', 's', 't', '!']

Here we can see how * operator made the split of the symbols from the string. Strings are iterable in Python, so it’s why we got such a result after unpacking. 

Take a minute, and think how the next example differs from the example above.

Here we can see how * operator made the split of the symbols from the string. Strings are iterable in Python, so it’s why we got such a result after unpacking. 

Take a minute, and think how the next example differs from the example above.

*example, = ["KoderShop is the best!"]
print(example)

#Output:
#['KoderShop is the best!']

Yes, you are right. In one line we got the * operator then a variable and then a comma. We created a new list called example, which takes the “KoderShop is the best!”. The comma makes a thing. When we use the asterisk operator for assigning a new variable, Python requires that the result be a list or a tuple. With the following comma, we have already defined that we have a tuple with the name: example, and which letters are the same as in the example before that: [‘K’, ‘o’, ‘d’, ‘e’, ‘r’, ‘S’, ‘h’, ‘o’, ‘p’, ‘ ‘, ‘i’, ‘s’, ‘ ‘, ‘t’, ‘h’, ‘e’, ‘ ‘, ‘b’, ‘e’, ‘s’, ‘t’, ‘!’].

NET MAUI as a Universal Development Platform

NET MAUI as a Universal Development Platform

NET MAUI
NET MAUI Technology

NET MAUI – Let’s Explore What Is It

At the stage of software design, one of the main points is the environment in which the software product will work. It can be a mobile device, a tablet, or a computer running Windows, Linux, or Raspberry PI operating systems. And what if we need a universal environment where we can run our application without being tied to a device? The .NET MAUI platform comes to the aid of the developer. Let’s talk about this in more detail. The abbreviation MAUI stands for Multi-platform App UI. This system is used to develop user interfaces for both classic desktop applications and mobile devices.

Implemented the NET MAUI platform in the form of 2 types of projects:

  1. Based on Blazor technology.
  2. Based on XAML technology.

NET MAUI Characteristics

Despite the fact that the system is universal, it still has minimal system characteristics and does not support many outdated devices. Consider the minimum versions of operating systems:

  • Android minimum supported version is 5.0 (API 21) if using XAML and Android 6.0(API 23) if we use MAUI Blazor.
  • IOS – minimum supported version 10. If we use .NET MAUI Blazor – IOS 11 and above are required.
  • MacOS is the minimum version of 10.13. and mandatory use of MAC Catalyst.
  • Windows – Microsoft MAUI is available with version 1809 (Windows 10) and Windows 11 is supported.
  • Linux – at the moment it is known that there is support, but there are no minimum system requirements.

From this characteristic, we can notice that the Blazor variant is more demanding on operating systems. If we consider support for Linux systems, and this will work well, this opens up great opportunities for using the MAUI App on other devices, including TVs and projectors. MAUI developers get great development opportunities – code once – works everywhere.

Features of .NET MAUI Technology

A nice feature for MAUI developers is that this system supports hot reloading .NET. What does this mean? This is a feature that allows you to change the program code while the application is running without having to recompile and stop it. Everything happens in real time.

Also, for the convenience of developers, there is access to API and tools for a specific platform. This allows the use of sensors such as a compass, gyroscope, and accelerometer. It is also possible to receive complete information about the device – the level of connection with the Internet.

How the MAUI Architecture Works

Consider the architecture of this technology based on the image provided by Microsoft:

NET MAUI technology

Consider the sequence of code execution.

When running the code, our code interacts directly with the MAUI API. Next MAUI uses its API to use the target platform interface, however, the application code can use the API of the target platform function directly.

 

Next, consider compiling for each operating system:

  • Android – Compilation is from C# to Intermediate Language (IL) then JIT compilation to native assembly.
  • IOS –  compilation happens immediately to ARM assembly code
  • macOS – compile as under IOS with subsequent changes using the Mac Catalyst program
  • Windows – uses the WinUI 3 library to create a ready-made windows maui apps

Comparison of NET MAUI with Other Technologies

At the moment, the main contender for comparison is Xamarin. For a more detailed comparison, consider this technology.

Xamarin is an open-source technology that allows you to create high-performance applications on different architectures at a level of abstraction that allows you to provide control between the main code and the code of the underlying platform, being a link. Does it sound familiar? The question arises: why then should we use NET MAUI? This platform is an evolution of Xamarin Forms – no need to create different projects, 1 project for all operating systems. There is no need to change the logic moving away from the operating system, there is no dependency on the file system either.

Let’s Summarize

 In this article, we examined the main features of the new progressive technology by Microsoft – .NET MAUI. This technology makes it possible to significantly facilitate the development of software, at the moment when the project is simultaneously needed on different operating systems and devices: phones, computers, tablets, and even TVs.

How To Migrate From ASP.NET To ASP.NET Core Losslessly

How To Migrate From ASP.NET To ASP.NET Core Losslessly

ASP NET to NET CORE migrating

Migrating from ASP.NET to ASP.NET Core

Good afternoon, in this article we will look at the basic concept of migrating from ASP.NET to ASP.NET Core. Let’s consider the main possibilities and expediency of this transition. Let’s talk about the benefits, of which there are plenty.

The first and probably one of the most important advantages is cross-platform: all the most famous operating systems are supported, both in the direction of Windows and Unix.

The second and no less important factor is modularity, the system is completely delivered in the form of Nuget packages, which in turn allows you to optimize the application, while simultaneously accelerating both the launch and updating of individual parts, essentially similar to the designer. Additionally, this allows you to integrate additional functionality in less time.

Now let’s say a few words about performance, with the release of each new version of ASP.NET Core, performance grows and noticeably. For example, ASP.NET Core processes 2300% more requests per second than ASP.NET. These huge differences in query speed are brought about by high performance modular conveyor – Middleware. In this implementation, each part of the middleware processes the HTTP request and then decides whether to return the result to the source or pass it to the next part of the middleware.

Based on the above qualities, this transition is appropriate, especially when the load increases and the project is scalable.

Differences in projects

Structure file

The main project structure file is a file with *. csproj. And compared to ASP. Net in ASP. Net Core has been modified and optimized. Let’s look at the main differences. The first thing that catches your eye is the absence of identifiers for other projects, which improves the readability of the code, the file itself is well-edited inside the Visual Studio environment. And also a good difference is the absence of an explicit specification of files, which in turn reduces the risk of conflicts when combining XML.

Point of entry

Previously in ASP. NET, the entry point for an application was the Global.asax file. It performed the tasks of configuring routes and registering filters. One approach is to link the application and the backend that hosts the application, which is not good.

To reduce this relationship, OWIN was introduced. This tool provides a more correct way to use multiple Frameworks, and also allows you to add only modules that are currently needed to the container request. The environment itself uses Startup in terms of service configuration. The startup, in turn, registers middleware services along with the application. The intermediate service component has the ability to add one or more handlers to the processing pipeline. At the same time, the handler, having finished its work, calls the next handler.

What ASP.NET Core downloads, the entry point in the application is the Startup class and this removes the dependency on Global.asax. However, keep in mind that Startup must include the Configure method with a description of the services that will be used in the application.

Storing settings

In Asp. Net, we use Web.config to store settings. by writing the settings to the < appSettings > section as key-value pairs.

Example:

< appSettings >
<add key="Login" value=" LoginValue " />
<add key="Password" value=" PasswordValue " />
</ appSettings >

In this context, we can access variables through the ConfigurationManager of the System.Configuration namespace.

What ASP.NET Core is downloading is that we are not tied to either a section or a file. We can store values in any file and load them using services on initial load. However, we have a default file in the project directory called appsettings.json.

Static files

In terms of working with files, you can also see differences in terms of optimization. Previously, in ASP.NET, such files were stored in various directories and on them we got ref.

However, in ASP.NET Core, such files are already stored in the “wwwroot” root directory, and this can already be optionally changed by the settings. You can call work with files at startup by writing app.UseStaticFiles (); in the Configure method.

Conclusions

In this article, we looked at the main features of ASP. NET and ASP. Net Core for migration. ASP.Net Core technology is actively developed and has a more optimized structure for work, and given the increase in processing speed, this makes it very effective in large projects.

E-Commerce Website Testing Case with a Step-by-Step Checklist Overwiev

E-Commerce Website Testing Case with a Step-by-Step Checklist Overwiev

E-Commerce Website Testing Process with Cases

E-commerce testing

How to Test an Online Store or Application

I bet you won’t find someone in today’s world who hasn’t done any online shopping. A business that relies on its online clients is e-commerce or retail. There are several benefits to buying in person as opposed to online. Convenience, time savings, simple access to goods around the world, etc.

The success of an e-commerce or retail site depends on it. It must be a deserving match for the storefront. Because when you shop at a physical store, you’ve already committed to going there and could even give the brand a shot.

Online options are plenty. Therefore, if interaction isn’t present right away, the user can decide to quit. The business will grow the better the website is.

It is crucial that the application undergoes careful testing because so much depends on it.

Types of Ecommerce Testing

E-commerce test cases

You must test every element of your overall e-commerce system. Typical test formats include:

Type

Session Management

Page Display

Browser Compatibility

Analyzing Content

Usability

Backup and Recovery

Transactions

Processing Orders

Server Testing

Security

What the Procedure Checks

  • The volume of data kept during a session is known as session storage.
  • How long until a session expires is known as the session expiration time.
  • Examining any potential runtime issues
  • Inappropriate font sizes or dead links
  • Dependency on subpar plugins: which should I disable and which should I keep on?
  • Slow downloading of pages
  • Compatibility issues with some browsers
  • A poor user experience with particular browser addons
  • Dad testing on popular operating systems including Windows, Mac OS X, and Linux
  • Examining content for litigation risk or deceptiveness
  • Using only free stock photos
  • Any violation of copyright
  • Individualizing components
  • Detecting and eradicating outdated content
  • Bad design
  • Lack of assistance
  • Website links that can be followed
  • Examining link locations
  • Time between backups
  • Periodically restoring backups to check for them
  • Mistake tolerance
  • Testing for monetary exchanges
  • Record-keeping and auditing
  • Checking the shopping cart’s operation
  • Processing transactions
  • Following orders
  • Orders are recorded
  • Examining the uptime
  • Server stress testing
  • Executing updates
  • Analysis of scalability
  • Ensure that your login credentials are updated frequently.
  • DDoS analysis
  • Computer worms
  • Making sure data is encrypted

E-Commerce test cases

Below, we have listed important segments and test cases for eCommerce website testing.

1. Hero section – Homepage case

Retail websites’ homepages are busy. There is a lot going on there. However, nearly all of them have a Hero Image:
This type of clickable image (which functions as a sort of slideshow) takes up the majority of the page.

In e-commerce, a homepage is more than just a well decorated cover. It also shows promise as a marketing strategy. This page typically has auto-scrolling slideshows or clickable banners that direct readers to particular pages. QA engineers examine the logo, top navigation for logged-in and unlogged-in visitors, and the keyword search when testing a homepage. A QA team’s job is to evaluate the features, page layout, and content visibility. The latter include advertisements such as banners, links to newsletters and social media platforms, and so forth. Here are some examples of test cases for an online store:

  • The speed of page loading is sufficient.
  • The time it takes for a user to log in with the proper credentials is reasonable.
  • The homepage’s typefaces and color scheme are typical.
  • Numerous browsers support controls.
  • The scrolling interval and the carousel automatically scroll.
  • A CTA button or banner click directs a user to the desired page.
  • The links direct visitors to the appropriate pages.
  • The shopping cart, Log In button, and Sign Up button are all clearly marked.

Therefore, homepage test cases essentially include some of the logging, navigation, and UI test cases that we will discuss in more detail later in the text.

2. Search case

Because we can’t always put what customers want to see directly in front of their eyes, search algorithms are crucial to a retail site’s success.

  • Search based on the name of the product, the brand, or, more broadly, the category.
  • Relevant search results are required.
  • There must be a variety of sort choices, including those based on Brand, Price, Reviews, and Ratings, etc.
  • How many results per page should be shown?
  • Are there methods to browse to multi-page results?
  • Additionally, search occurs everywhere. When validating this functionality, kindly take the search drilling down into various levels into account.

3. Sorting and filtering case

These two marginally different but closely connected characteristics significantly improve the use of an e-commerce website. The importance of search filters increases with the size of the item selection, especially for mobile versions. Users prefer to leave a website rather than browse a list of useless suggestions because they find constant scrolling to be annoying. Users can move between categories and sub-options using the filtering feature, and the precise results are then shown in front of them. Users can arrange products in the desired order by sorting them according to criteria such product name, brand, price, etc. QA engineers should be aware of the following in order to provide a flawless experience:

  • A user’s application of a new filter does not disable an existing filter.
  • There is no restriction on how many filters a person may employ.
  • The application of filters is immediately and clearly verified.
  • At the top of the list is a summary of all applied filters.
  • Users can easily get rid of the selected options.
  • All things show in the desired order once a user uses the sorting tool.

4. Shopping cart case

One of the important areas that must be thoroughly tested is the shopping cart. Visitors to a website won’t stay if they can’t add products to their carts. As a result, testing scenarios should include a variety of procedures with a selected item. In other words, the functionality of the shopping cart occasionally needs to handle computations that are fairly complicated. Promotional periods are fixed, there are rules for coupons, vouchers, and discount codes, and everything must work with the logic of the shopping cart.

5. Test cases for the checkout flow case

Because of the wide range of payment methods available nowadays, this step in the purchasing process might be challenging. Making sure the merchant allows the usage of several choices, such as Visa, Mastercard, PayPal, Apple Pay, Google Pay, etc., is the responsibility of QA engineers. In the meantime, the website should also automatically determine the overall cost by applying any applicable fees that the selected payment option entails (if any).

  • Before finishing the transaction, you are prompted to log in or register.
  • Customers can check out and make payments as unregistered guests.
  • Logged-in users who log in again can use previously saved shipping and billing information.
  • Every supported payment method operates as intended.
  • The prices are accurate, even if a particular payment option carries additional fees.
  • After the payment, sensitive data, including payment information, is not kept on file.
  • A successful checkout results in the appearance of an order confirmation page.
  • An email or text message is used to deliver an order confirmation message to the user.
  • Users with an account can view their order status.
  • A user can continue exploring the website after completing the payment.

One of the important areas that must be thoroughly tested is the shopping cart. Visitors to a website won’t stay if they can’t add products to their carts. As a result, testing scenarios should include a variety of procedures with a selected item. In other words, the functionality of the shopping cart occasionally needs to handle computations that are fairly complicated. Promotional periods are fixed, there are rules for coupons, vouchers, and discount codes, and everything must work with the logic of the shopping cart.

6. The account user test case

For a variety of reasons, users might need to update their personal information. It can be due to a credit card’s expiration, a change in the shipping address, an error during registration, etc. Below are a few of test case examples:

  • The My Account section and associated settings are accessible to logged-in users.
  • A user’s account information, such as contacts, a shipping address, a password, and other details, can be updated and modified.
  • In the My Orders section, a user can view and/or manage order status
  • . Previous orders can be viewed and repeated by users.
  • By pressing the matching button, a user can log off.

7. User Interface  case

UX and UI testing frequently go hand in hand, but we’ll talk about them individually. So all the visual components that enable user interaction with the programmed functionalities are covered by UI test cases. UI components that you could encounter on many websites include:

  • buttons;
  • links;
  • checkboxes;
  • radio buttons;
  • dropdown lists;
  • toggles;
  • text fields;
  • date pickers;
  • search fields;
  • tags;
  • sliders;
  • pagination;
  • tooltips;
  • accordions;
  • input fields, etc.

It is crucial to evaluate layout, icons, and images for visual coherence in addition to the functioning of UI elements. This category also includes output elements like pop-up windows, alerts, badges, and notifications. Here are some sample test cases you ought to be aware of in relation to:

  • The signing and logging sections both allow users to submit their credentials.
  • Text can be entered by users in the text areas, including reviews and comments.
  • Each button can be clicked to carry out the pre-programmed functions.
  • The correct pages can be reached by clicking on the navigational elements such as breadcrumbs, tags, and others.
  • After a click or slide, the position of the toggles changes, and each position can be seen.
  • The performance and appearance of UI elements are not device-dependent.

This list is lengthy and contains many different items based on the conditions and pages. A user might only be permitted to check a certain amount of checkboxes, for instance. Therefore, before writing test cases, be sure to carefully review the requirements.

8. User Experience Case

Businesses and online business owners have recently tried to concentrate more on usability and user experience. While an e-commerce website’s visual design contributes to a brand’s overall image, it is important to ensure that it is simple and easy to use. The tests cases listed below assist with this task:

  • The sorting function takes into account all the important factors.
  • It is simple to navigate between pages if search results span more than one.
  • Information may be read easily thanks to the font’s size and color.
  • Product descriptions are accurate and pertinent.
  • Relevant products can be found on all category pages.
  • The page is easy to navigate for the user without having pop-ups obscure much of the content.
  • The branding overall, as well as the design, are consistent across a variety of platforms and screens.

The basic UX test scenarios don’t stop with these. This kind of testing necessitates in-depth user research and perhaps business analysis for productivity gains.

Summary

A website must function on mobile devices in addition to computers. It must be secure and responsive. A data warehouse that supports OLAP and BI should be maintained with the use of ETL operations and an efficient database. Testing for e-commerce should concentrate on all of that. However, whether or not visitors convert to paying consumers is what matters most in e-commerce testing. The conversion rate is the proportion of visits that result in a sale.

What Is Indexing In MongoDB – How to Create and Apply Correctly

What Is Indexing In MongoDB – How to Create and Apply Correctly

MongoDB indexing

MongoDB indexing – your small but important database helper

With the help of a database management system for documents, on MongoDB, you may store a lot of data in documents with different sizes and structures. With the help of MongoDB’s robust querying capabilities, you may filter documents according to predefined criteria. However, as a MongoDB collection expands, finding documents might be like looking for a needle in a haystack.

Due to MongoDB’s query flexibility, it might be challenging for the database engine to anticipate the kind of queries that will be used the most frequently; instead, it must be prepared to find documents regardless of the collection’s size. Because of this, search speed is directly impacted by the volume of data contained in a collection: the larger the data set, the more challenging it is for MongoDB to locate the documents that match the query.

An index is one of the most important tools a database administrator can employ to consciously support the database engine and enhance its performance. You’ll learn about indexes in this article, as well as how to make them and inspect how they’re used when the database runs queries.

How do you create an index in MongoDB?

By means of MongoDB’s built-in shell interface, you can create indexes. Nonetheless, indexes do not exist in isolation. Before creating an index, you must first construct a collection and then a database. In MongoDB, single indexing is simple. Pick a database that has collections in it to get started.

Lets think that our database is named DATA:

use DATA

Once we choose a certain database, we use the createIndex() command procedure to create just a single index.

For example, build one username index in a collection and use it for reverse-order searching:

db.collectionName.createIndex({username: -1})

Every time you query data using the age index, MongoDB is instructed to organize age in reverse order by the negative number (-1) in the code. As a result, since the index takes care of that during queries, you might not need to specify a sorting order.

MongoDB Multikey Indexing

For the purpose of indexing a field in a complicated data array, multikey indexes are useful. For instance, a collection can have intricate user data with that data contained in a separate array. Say your name, height, and age as an example.

The height of each user in that array can be used to generate a multikey index in the following way:

db.customers.createIndex({user.height: 1

The user field’s subset, height, is shown in the code above.

Creating compound index

Multiple indices are combined to form a compound index. For example, in order to build a compound index of address and product category for a customer collection:

db.customer.createIndex({address: 1, products: 1})

Because you didn’t specify a name when building the aforementioned index, MongoDB generates one automatically. However, an underscore is used to denote each index’s name. Therefore, it is less understandable, especially if a compound has more than two indexes.

When building a compound index, be sure to provide a name:

db.customers.createIndex({location: 1, products: 1, weight: -1}, {name: "myCompundIndex"})

In order to see every index in a collection:

db.collectionName.getIndexes()

The above line of code outputs all the indexes in the certain collection.

The dropIndex() method

Using MongoDB’s dropIndex() procedure, you can remove a specific index.

>db.COLLECTION_NAME.dropIndex({KEY:1})

The name of the file on which you want to delete an existing index is “key” in this case. You may alternatively specify the name of the index directly in place of the index specification document (above syntax) as follows:

dropIndex("name_of_the_index")

The dropIndexes() method

With this procedure, a collection’s numerous (specified) indexes are removed.

The DropIndexes() procedure has the following basic syntax:():

>db.COLLECTION_NAME.dropIndexes()

The getIndexes() method

The description of each index in the collection is returned by this procedure.

db.COLLECTION_NAME.getIndexes()

The getIndexes() method

The description of each index in the collection is returned by this procedure.

Conclusion

By reading this article you got an understanding of of the concept of indexes, which are particular data structures which can make your query performance better by decreasing the amount of data MongoDB should analyze.