Selenium vs Scriptless Test Automation

Selenium vs Scriptless Test Automation

The Comparison Battle Concludes! It’s time to reach our conclusion. Selenium vs Scriptless Test Automation: What is best?

Software Testing has not evolved to meet the demands of the current accelerated delivery needs. As software becomes sophisticated, traditional approaches to Testing would not be sufficient. With the advancement in technology, Test Automation was introduced to accelerate the process of testing simply by automating test cases and scenarios.

In today’s world, almost every organization is onto Test Automation for better business results.

For the past few years, the First generation of Test Automation has been in the spotlight. These provide businesses and testing teams a macro recording which runs in parallel with an Application Programming Interface (API), whereas test execution engines send a command and wait for it to be executed. This tool was working well for short length automation and was also done by combining a logical sequence of such short length macros and test flows.

Then came the Second generation Test Automation Tools that offers full scripting capability with more features and better support for user interaction. Most of the windows based on common tasks such as operations, application, launch, generating reports were simplified. Frameworks like these are required to be built on long term test automation to go at par with quality the business is expecting.

As the continuous innovation in technology, Scriptless Test Automation is rapidly gaining its popularity amongst various IT companies – simply because it is designed to help the testing team to quickly build ready-to-use automated test cases and reusable code assets that have full test coverage.

Second-Gen Test Automation Tool, “Selenium”

Selenium is a suite of web browser automation tools to automate browsers across a range of platforms. It is principally used to automate web apps for testing reasons even though it is capable of so many things. The majority of big-name browser vendors support it. Furthermore, many of these either have taken or are taking steps to make it a native part of their browsers. Besides, it is the core technology used in serried of other APIs, browser automation tools, and automation testing frameworks.

Selenium runs on various browsers and operating systems, and a range of programming languages and frameworks can control it. Plus, it can automate tedious web-based administration tasks.

It also provides for authoring tests without the necessity to acquire knowledge of a test scripting language (Selenium IDE). Selenium also provides a test domain-specific language (Selenese) to write tests in several popular programming languages, including Scala, Groovy, Java, Ruby, C#, PHP, Python, and Perl. The tests can then run against current and newest web browsers. Selenium deploys on Windows, macOS and Linux platforms.

Selenium is primarily composed of respective components with specific role assists the web application test automation development. These are Selenium Grid, Selenium IDE (Integrated Development Environment), Selenium WebDriver, and Selenium RC (Remote Control).

Scriptless Test Automation Tools: A “Less to Zero Coding” for QA Professionals

Scriptless” implies “no scripting and programming”. This is somewhat misleading to the audience especially to those who are new to software testing. Scriptless testing doesn’t eliminate scripting and is not suitable for the actual coding of any test automation tool. It is an extremely flexible testing framework with minimal exposure to code. In layman’s terms, it can be said that scriptless testing is an approach to conduct testing without scripting or coding in any programming language. It reduces the time required for creating automated tests by considerably minimizing the amount of scripting needed.

Scriptless Test Automation is a more structured model where the majority of test cases can be automated by functional users and those are reusable scripts. It is a myth that test architects will be involved in scriptless testing but in real-time they are not. Based on their expertise and knowledge, test architects will be involved in identifying the possible reusable part of the test scripts. When operational scope spreads out, few new components will be added to the application and then test architects will be involved to integrate them.

Scriptless Test Automation is being refined to highly organized tools for use in multiple real-time test scenarios. The developers of those tools shall have analyzed various business cases, deployment environments, operational scenarios before building each reusable components for their tools. Hence they offer a good standard of practical reliability as well.

With Scriptless Test Automation, testers can create better automation scripts that showcase the core functionality of the application. This type of testing will certainly reduce the number of licenses for the testing tools. Scriptless Test Automation will be more useful from product development companies where the same resources can be leveraged for multiple tasks.

Thought it has many advantages, there are scriptless test automation frameworks that have complex underlying codes that need to be maintained and updated periodically. Those frameworks are not free – they consume initial and maintenance costs which is more than the tool’s cost. Therefore, choosing the right framework specific to your business requirement is the only manual effort you need to do.

Using Scriptless Test Automation tools, the testing team does not need any coding or scripting knowledge to create automated tests. However, as a framework, it is a good practice to provide the capability of customization.

For advanced users, customization can be done by writing their code for covering some unique test scenarios.

Selenium Vs Scriptless Test Automation Tool: What is Best?

Selenium is required to be built on long term test automation to go at par with quality. For instance, if the testing team wanted to reuse test scripts then it is best to leverage Selenium IDE. Selenium IDE is one of the components of Selenium capable of recording, editing and debugging of functional test scripts. Test scripts have to be made from scratch by the testers, therefore, they are required to know programming or scripting.

On the other hand, Scriptless Automation Tools already provide ready-to-use automated test cases and reusable code objects. Mostly for Scriptless Test Automation tools, these features are out-of-the-box. It is not mandatory for the testers to know programming or scripting of some sort. It empowers testers and business users to automate testers without worrying about coding. The problem of coding is largely taken care of by the Scriptless Test Automation tool already.

In terms of debugging test scripts, Scriptless Test Automation tools eliminate the need to repeatedly debug scripted test code as this already done by the tool itself, while in Selenium, automation testers when encountering any test script errors have to debug the test code manually.

Setting up test environments for Selenium is difficult. Selenium uses various plugins to support Test Automation thus making it a must for system owners and administrators to update and configure everything manually. Scriptless test environments can be built faster and maintained easily creating a hassle-free update environment.

Hence, making a Scriptless Test Automation tool a perfect fit for Test Automation for Agile and DevOps.

Using Scriptless Test Automation Tool, the testing team can easily automate large test suites faster almost double the times faster compared to Selenium. It greatly reduces the “go to market” time, which comes in handy when there are major software updates. In short, the more the test coverage, the better as it produces a well-refined product which is a win-win situation for the business.

Scalability and Flexibility is one of the most desirable in Scriptless. It is flexible to work with various browsers available in the market and it scales an amount of workload as much as the business requires. For Selenium to be flexible enough to work with multiple browsers, it needs to have components

The Comparison Battle Concludes!

It’s time to reach our conclusion. Both contenders, Selenium and Scriptless Test Automation Tools do what they do best. **Now, which is better? **

Overall, Scriptless Test Automation Tools have an edge over Selenium. Scriptless test automation tools can easily be set up, configured and maintained and does not require any plugins as Selenium does. It can handle the bulk amount of test cases way faster than Selenium.

Scriptless Test Automation Tools do not require that their functional users know scripting and programming. Users can conduct their QA activities with “ready-to-use” test steps which already exist within the platform.

Unlike Selenium, one needs to add Selenium IDE to leverage playback tool which grants users feature slightly similar with reusable codes scriptless has. If we talk about scalability and flexibility, scriptless is the one in favor. Everything you need is already available. Selenium, for the most part, requires components to utilize and fully leverage it.

The battle is over. If you are looking for the perfect Automation Testing Suite and a complete solution fit for Agile and DevOps environments, then a Scriptless Automation Tool is definitely for you.

When we start talking about Test Automation, we can create a list of issues every business is facing and some which are critical for the business. Some of the major pain points include the usage of multiple tools required in testing sessions, rigorous scripting and coding, test automation frameworks, test lab, integrations and other QA related concerns that need to be addressed. Even with all these, your testing team is all over the place. It’s time to say goodbye to all this.

Say hello to a different approach to Scriptless Test Automation different from Record and Playback Tools, Testsigma, a complete Test Automation ecosystem!

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning. We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning

We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning

Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning

Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.

But still, there is a big misconception among many people about the meaning of these terms.

In the worst case, one may think that these terms describe the same thing — which is simply false.

A large number of companies claim nowadays to incorporate some kind of “ Artificial Intelligence” (AI) in their applications or services.

But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.

On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.

An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:

Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial Intelligence

Machine Learning vs Deep Learning

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ classical” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

At this point, you may ask: How do we minimize the error?

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is old…

The basic definition of machine learning is:

Algorithms that analyze data, learn from it and make informed decisions based on the learned insights.

Machine learning leads to a variety of automated tasks. It affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for cheap trades. Machine learning requires complex math and a lot of coding to finally get the desired functions and results.

Machine learning algorithms need to be trained on large amounts of data.
The more data you provide for your algorithm, the better it gets.

Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.

These classic algorithms include algorithms such as the so-called Naive Bayes Classifier and the Support Vector Machines. Both are often used in the classification of data.

In addition to the classification, there are also cluster analysis algorithms such as the well-known K-Means and the tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tSNE.

Deep Learning — The next big Thing

Now let’s focus on the essential thing that is at stake here. On deep learning.
Deep Learning is a very young field of artificial intelligence based on artificial neural networks.

Again, deep learning can be seen as a part of machine learning because deep learning algorithms also need data to learn how to solve problems. Therefore, the terms of machine learning and deep learning are often treated as the same. However, these systems have different capabilities.

Deep Learning uses a multi-layered structure of algorithms called the neural network:

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.

All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.

We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.

Why is Deep Learning better than Machine Learning?

Feature Extraction

The first advantage of Deep Learning over machine learning is the needlessness of the so-called Feature Extraction.

Long before deep learning was used, traditional machine learning methods were popular, such as Decision Trees, SVM, Naïve Bayes Classifier and Logistic Regression. These algorithms are also called “flat algorithms”.

Flat means here that these algorithms can not normally be applied directly to the raw data (such as .csv, images, text, etc.). We require a preprocessing step called Feature Extraction.

The result of Feature Extraction is an abstract representation of the given raw data that can now be used by these classic machine learning algorithms to perform a task. For example, the classification of the data into several categories or classes.

Feature Extraction is usually pretty complicated and requires detailed knowledge of the problem domain. This step must be adapted, tested and refined over several iterations for optimal results.

On the other side are the artificial neural networks. These do not require the step of feature extraction. The layers are able to learn an implicit representation of the raw data directly on their own.

Here, a more and more abstract and compressed representation of the raw data is produced over several layers of an artificial neural network. This compressed representation of the input data is then used to produce the result. The result can be, for example, the classification of the input data into different classes.

In other words, we can also say that the feature extraction step is already a part of the process that takes place in an artificial neural network. During the training process, this step is also optimized by the neural network to obtain the best possible abstract representation of the input data. This means that the models of deep learning thus require little to no manual effort to perform and optimize the feature extraction process.

For example, if you want to use a machine learning model to determine whether a particular image shows a car or not, we humans first need to identify the unique features of a car (shape, size, windows, wheels, etc.), extract these features and give them to the algorithm as input data. This way, the machine learning algorithm would perform a classification of the image. That is, in machine learning, a programmer must intervene directly in the classification process.

In the case of a deep learning model, the feature extraction step is completely unnecessary. The model would recognize these unique characteristics of a car and make correct predictions- completely without the help of a human.

In fact, this applies to every other task you’ll ever do with neural networks.
They just give the raw data to the neural network, the rest is done by the model.

The Era of Big Data…

The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:

The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.

Deep Learning models scale better with a larger amount of data

Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.

Special Announcement: We just released a free Course on Deep Learning!

I am the founder of DeepLearning Academy, an advanced Deep Learning education platform. We provide practical state-of-the-art Deep Learning education and mentoring to professionals and beginners.

Among our things we just released a free Introductory Course on Deep Learning with TensorFlow, where you can learn how to implement Neural Networks from Scratch for various use-cases using TensorFlow.

If you are interested in this topic, feel free to check it out ;)

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning

In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning.

Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning.

Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.

But still, there is a big misconception among many people about the meaning of these terms.

In the worst case, one may think that these terms describe the same thing — which is simply false.

A large number of companies claim nowadays to incorporate some kind of “ Artificial Intelligence” (AI) in their applications or services.

But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.

On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.

An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:

Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial Intelligence

Machine Learning vs Deep Learning

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ classical” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

At this point, you may ask: How do we minimize the error?

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is old…

Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.

Some known methods of classification and prediction are the Naive Bayes Classifier and the Support Vector Machines. In addition to the classification, there are also clustering algorithms such as the well-known K-Means and tree-based clustering. To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and tSNE are used.

Deep Learning — The next big Thing

Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks.

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.

All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.

We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.

Why is Deep Learning better than Machine Learning?

The first advantage is the needlessness of Feature Extraction. What do I mean by this?

Well if you want to use a Machine Learning model to determine whether a given picture shows a car or not, we as humans, must first program the unique features of a car (shape, size, windows, wheels etc.) into the algorithm. This way the algorithm would know what to look after in the given pictures.

In the case of a Deep Learning model, is step is completely unnecessary. The model would recognize all the unique characteristics of a car by itself and make correct predictions.

In fact, the needlessness of feature extraction applies to any other task for a deep learning model. You simply give the neural network the raw data, the rest is done by the model. While for a machine learning model, you would need to perform additional steps, such as the already mentioned extraction of the features of the given data.

The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:

The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.

Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development

Python Tutorial for Beginners (2019) - Learn Python for Machine Learning and Web Development




TABLE OF CONTENT

00:00:00 Introduction

00:01:49 Installing Python

00:06:10 Your First Python Program

00:08:11 How Python Code Gets Executed

00:11:24 How Long It Takes To Learn Python

00:13:03 Variables

00:18:21 Receiving Input

00:22:16 Python Cheat Sheet

00:22:46 Type Conversion

00:29:31 Strings

00:37:36 Formatted Strings

00:40:50 String Methods

00:48:33 Arithmetic Operations

00:51:33 Operator Precedence

00:55:04 Math Functions

00:58:17 If Statements

01:06:32 Logical Operators

01:11:25 Comparison Operators

01:16:17 Weight Converter Program

01:20:43 While Loops

01:24:07 Building a Guessing Game

01:30:51 Building the Car Game

01:41:48 For Loops

01:47:46 Nested Loops

01:55:50 Lists

02:01:45 2D Lists

02:05:11 My Complete Python Course

02:06:00 List Methods

02:13:25 Tuples

02:15:34 Unpacking

02:18:21 Dictionaries

02:26:21 Emoji Converter

02:30:31 Functions

02:35:21 Parameters

02:39:24 Keyword Arguments

02:44:45 Return Statement

02:48:55 Creating a Reusable Function

02:53:42 Exceptions

02:59:14 Comments

03:01:46 Classes

03:07:46 Constructors

03:14:41 Inheritance

03:19:33 Modules

03:30:12 Packages

03:36:22 Generating Random Values

03:44:37 Working with Directories

03:50:47 Pypi and Pip

03:55:34 Project 1: Automation with Python

04:10:22 Project 2: Machine Learning with Python

04:58:37 Project 3: Building a Website with Django


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Further reading

Complete Python Bootcamp: Go from zero to hero in Python 3

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python and Django Full Stack Web Developer Bootcamp

Complete Python Masterclass

Python Programming Tutorial | Full Python Course for Beginners 2019 👍

Top 10 Python Frameworks for Web Development In 2019

Python for Financial Analysis and Algorithmic Trading

Building A Concurrent Web Scraper With Python and Selenium