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Robot Framework Tutorial For Beginners | Robot Framework With Python | Intellipaat

Intellipaat・166 minutes read

Robot framework simplifies automation testing through keyword-driven approaches, while the course covers Python concepts, Selenium WebDriver, and frameworks like Data-Driven and Hybrid, emphasizing the importance of automation scope and feasibility. Python offers simplicity and flexibility in programming, allowing for various data types, loops, exception handling, and collection frameworks, with key concepts like classes, inheritance, and variables discussed in both Python and Java.

Insights

Robot framework simplifies automation testing by using action words as keywords, reducing the need for extensive scripting while still requiring some scripting.
The tutorial on mastering robot framework covers essential concepts such as automation understanding, Python basics, and Selenium WebDriver usage.
Vijay, the instructor, introduces the course curriculum, emphasizing no explicit prerequisites but suggesting Java familiarity for learners.
Automation aids in expanding test coverage, with critical business scenarios identified for automation, emphasizing comprehensive coverage of complex cases.
Frameworks play a crucial role in automation design, offering pre-determined operations, simplifying coding, and enabling immediate test case creation for new applications.

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Recent questions

What is Robot framework?
The Robot framework is a keyword-driven automation testing approach that simplifies test function arguments and minimizes coding for new test cases.
How does Python compare to Java?
Python is considered easier than Java due to its interpreter-based nature, offering simplicity, easy syntax, and library support.
What are the types of frameworks in automation testing?
The four types of frameworks in automation testing are Data-Driven, Keyword-Driven, Modular Automation, and Hybrid, each serving specific testing needs.
What is the purpose of CI/CD integration in automation testing?
CI/CD integration in automation testing eliminates manual intervention, triggering automated testing upon code commits for efficient testing processes.
How are variables treated in Python classes?
Variables inside Python classes can be defined as instance variables, accessed using "self," and can coexist with global variables without causing errors.

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Summary

00:00

Mastering Robot Framework for Automation Testing

Robot framework is a keyword-driven automation testing approach that reduces the need for scripting but still requires some scripting.
Action words are used in robot framework as keywords to simplify test function arguments and minimize coding for new test cases.
The video tutorial covers essential concepts for mastering robot framework in various testing scenarios.
The session agenda includes understanding automation, different automation approaches, basic Python functionalities, and Python concepts in unit testing.
Selenium WebDriver is discussed, followed by reading and writing data using set frameworks and drivers.
The instructor, Vijay, introduces himself and discusses the course curriculum, emphasizing no explicit prerequisites but suggesting familiarity with Java.
Python is compared to Java in terms of complexity, with Python being considered easier due to its interpreter-based nature.
Selenium WebDriver with Python is explained, highlighting similarities in commands but differences in library usage.
Data reading and writing methods in Python are discussed, noting differences from Java in terms of libraries used.
The course covers page object model, automation with Robot Framework, and using RIDE IDE for test script configuration and execution.

16:56

Maximizing Automation Efficiency in Test Execution

Speed of test execution is not increased by interacting with the application due to the time required for the application to perform operations.
Automation aids in expanding test coverage, dependent on the automation tester's ability to cover various scenarios comprehensively.
Manual testers transitioning to automation must identify critical business scenarios for automation, focusing on complex and tedious cases.
Newly designed test cases should not be executed manually before automation to ensure stability in the automation process.
Test cases executed on an ad-hoc basis, like exploratory or monkey testing, are not suitable for automation, which primarily focuses on functional and regression testing.
Factors determining automation feasibility include application stability, need for automation, and level of testing complexity.
Tool selection for automation is based on factors like return on investment, availability of resources, and licensing costs.
Defining the scope of automation involves preparing a proof of concept to validate the selected tool's compatibility with the application.
Test script preparation and execution follow successful implementation of the automation framework, with maintenance being a continuous process.
Automation scope should focus on automating core business functionalities, scenarios with large data sets, and applications with technical feasibility for automation.

34:15

Essential Frameworks in Design and Development

Frameworks are crucial in design and development, not limited to Selenium or APM.
TestComplete also utilizes frameworks, with QTP having its Automation Object Model.
Frameworks eliminate the need for classifying code, allowing immediate test case writing for new applications.
Frameworks pre-determine operations like generating logs, reading config files, and switching between windows.
Four types of frameworks are Data-Driven, Keyword-Driven, Modular Automation, and Hybrid.
Automation testing differs from manual testing in user involvement and random testing capabilities.
Automation testing is cost-effective for high-volume regression but not for low-volume scenarios.
CI/CD integration in automation testing eliminates manual intervention, triggering automated testing upon code commits.
Resource allocation in projects considers manual, automation, DevOps, and managerial resources.
Python, a high-level interpreter language, offers simplicity, easy syntax, and library support, widely used in organizations like Google, Netflix, and NASA.

52:34

JetFrames: Python IDE for Windows Users

The software, JetFrames, is available for download as an .exe file from IntelliJ.
JetFrames offers both professional and community editions for Windows users.
Installation of JetFrames is straightforward, requiring no additional setup after installation.
To configure JetFrames, users need to select the Python interpreter matching their C drive.
The default virtual environment in JetFrames is sufficient for usage, avoiding the need for additional configurations.
External libraries in JetFrames, akin to reference libraries in Java Eclipse, are stored in the site packages layer.
Installing tools like Selenium in JetFrames involves using the terminal within the project and running 'pip install -U selenium'.
JetFrames allows for the creation of projects and packages similar to Eclipse, with minor differences.
Python in JetFrames handles data types differently from Java, with no need for explicit declarations.
Python in JetFrames categorizes data types into numeric, boolean, set, sequence type, and dictionary, with specific distinctions within each category.

01:11:48

Python Data Structures Overview

Lists in Python are denoted by square brackets and can store any type of data.
Lists are ordered collections of data that can be of multiple types.
Garbage collection in Python does not occur automatically.
Accessing values in a list is done based on index, including negative indexing.
Tuples in Python are similar to lists but use curly brackets and cannot be updated.
Tuples are used for static data that should not be changed.
Nested tuples are possible, allowing for grouping of data.
Booleans in Python are case-sensitive, with 'True' and 'False' being the correct format.
Sets in Python do not allow duplicate values and are unordered collections.
Dictionaries in Python are similar to maps and allow for multiple values for a single key.

01:32:47

Python Loops: Key Concepts and Implementation

Implementing a while loop in Python is similar to Java, with the key difference being the use of colons instead of parentheses.
In Python, the else part can be provided in a while loop, which executes if the condition fails.
The while loop executes first, followed by the else part if the condition is met, incrementing the count.
The pass statement in Python is used for writing empty loops or control statements that do not require any action.
For loops in Python can be implemented using the range function, specifying the start and end points for iteration.
Lists in Python can be iterated using for loops, with the ability to loop through each value directly.
Dictionaries in Python can also be iterated using for loops, accessing key-value pairs for iteration.
Nested loops in Python can be achieved by nesting for loops within each other for complex iterations.
The continue and break statements in Python can be used to skip or exit a loop based on certain conditions.
Exception handling in loops can be done using try-except blocks to handle errors or stop iterations.

01:55:03

Python Programming: If Statements, Strings, Arrays

Conditions in programming are defined using if statements, with alignment indicating belonging to the if block.
Nested if statements can lead to issues with line indentation, requiring careful attention to avoid problems.
The elif statement is used in Python as an alternative to else if, maintaining similar functionality.
Shortened operators, like shorthand if statements, allow for concise coding when only one statement needs execution based on a condition.
Strings in Python are collections of Unicode characters, with concatenation operators like + used for merging strings.
The join method in Python is an alternative to concatenation, inserting a specified string between elements being joined.
The % operator in Python is used for string formatting, allowing for well-formatted strings.
The format method in Python provides another way to format strings, offering flexibility in string manipulation.
Arrays in Python are similar to lists but require specific data type declarations, with methods like pop and reverse available for array manipulation.
Input from users can be obtained in Python using the input function, allowing for dynamic array creation based on user input.

02:16:04

"Python Array Manipulation with Numpy Functions"

To add values to an array, use the "append" function under the array, copying the input value to variable x.
Use the "input" class to accept user input, reading it as a string by default.
To search for a specific value in an array, use a loop to check each element until a match is found.
For two-dimensional arrays, install the "numpy" package for array computing in Python.
Numpy allows treating arrays as lists, automatically identifying data types based on input values.
Numpy provides functions like "linspace" for evenly spaced numbers over an interval, "arange" for setting a range, and "logspace" for logarithmically spaced values.
Numpy simplifies operations like copying arrays, adding arrays, finding minimum or maximum values, and concatenating arrays.
Numpy offers shallow and deep copy options for copying arrays, with deep copy creating a new memory object.
Use the "dtype" function to determine the data type of an array, which can change based on input values.
Functions in Python start with "def" followed by the function name and can accept arguments, with the ability to use keyword arguments for flexible data passing.

02:36:43

Python Programming: Key Concepts and Best Practices

To overcome the question analysis issue, passing with the keyword "age," "place of origin," and "mobile number" is suggested.
Adding an additional asterisk will help in identifying and storing information as key-value pairs.
To fetch data, one can access values using the keyword "j" and omitting the "ipad."
Variables inside a method are treated differently; local variables can coexist with global variables without causing errors.
Passing a list as a parameter is possible, allowing for various operations like checking for even or odd numbers within the list.
Python classes are similar to Java classes, created using the keyword "class" and defining variables within the class as class or static variables.
Objects in Python are instances of classes, created without the "new" keyword, and methods within classes have an additional parameter, "self," referring to the current class.
Constructors in Python are created using the "__init__" method, used for initializing object states.
Instance variables in Python are data variables whose values are assigned within methods or constructors, not at the class level.
Instance variables can be accessed within methods using "self," and methods can be defined to set or retrieve instance variable values.

02:59:58

Java Constructors, Variables, and Polymorphism Explained

In Java, a default constructor is automatically created, even if not explicitly defined, resulting in an empty constructor.
Overwriting the default constructor with a print statement allows for custom output when called.
The "self" parameter in Java refers to the current class reference, essential for method definitions.
Global or class variables in Java do not require the "static" keyword but behave similarly to static variables by default.
Instance variables, defined within a class, are distinct from local and global variables in Java.
Accessing class variables in Java can be done using the class name directly, without needing an object.
Scope of variables in Java is crucial, with local variables having limited scope within methods.
In Java, method overloading is a form of polymorphism achieved by having methods with the same name but different signatures.
Polymorphism in Java can also be achieved through inheritance, allowing for method overriding in subclasses.
Subclassing in Java involves a child class identifying its parent class to access superclass information, enhancing the inheritance concept.

03:22:06

Python Inheritance and Class Creation Basics

The object as parent is used to create the root of all classes, defining a person as the ancestor class.
Creating a class called Person involves defining a constructor with parameters like name and ID.
Inheriting from the Person class to create a Child class, additional parameters like salary and designation can be added.
Instantiating an object from the Child class involves passing values for name, ID, salary, and designation.
Python supports single and multiple inheritance, with multiple inheritance requiring the use of brackets to inherit from multiple parent classes.
Multi-level inheritance involves extending classes in a nested manner, similar to Java's nested inheritance.
Hierarchical inheritance allows for creating multiple derived classes from a single base class.
Hybrid inheritance combines different forms of inheritance like single, multiple, or multi-level with hierarchical inheritance.
Private variables can be defined using double underscores to restrict access within the class.
The Collections framework in Python includes classes like Counter, OrderedDict, defaultdict, ChainMap, and more for managing different types of elements efficiently.

03:45:24

Combining dictionaries with ChainMap and named tuples

ChainMap involves creating a list of dictionaries, combining all key-value pairs from each dictionary into one list without splitting them.
To add a new dictionary to a ChainMap, use the `new_child` method, which adds the new dictionary to the beginning of the ChainMap.
By utilizing ChainMap with dictionaries `d1` and `d2`, then adding `d3` as a new child, the resulting ChainMap will contain all three dictionaries.
Named tuples provide objects with named positions, unlike regular tuples, allowing for easier access and updating based on names rather than indexes.
Conversion operations like `_make` and `_asdict` are crucial in transforming a list into a named tuple and a named tuple into an ordered dictionary, respectively.

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