Software quality is achieved through a combination of manual methods and automated tools. While critical thinking skills are irreplaceable when assessing quality, product releases cannot attain velocity and acceleration without the use of automation technology. Given today’s multi-dimensional products with complex architectures and scores of integrations, being built for a variety of platforms, operating systems, devices, and browsers, automation has become a critical factor for the success of the products and companies.
Automation kicks in early in the product development and testing life cycle, even as the independent components are being put together. Whether for code analysis, unit testing, component integrations, system performance, security posture, or business assurance, automation tools are being used across the spectrum of the end-to-end continuous testing pipeline.
So, where does one begin to acquire these automation skills? What are the tools to get familiar with? Do we need programming aptitude and knowledge of algorithms? What is the learning curve and the timeframe to acquire the basics? Where do I start?
One should try to improve on their programming language basics and coding skills first, and then move on to learning testing automation tools. A good developer would not take much time to learn or master automation tools/libraries like Selenium or Rest Assured. The aim should be to become a good programmer in the first place.
The following route is the learning curve to becoming an automation developer:
Timeframe for doing the same should be two months to learn the basics of programming language and coding practice and one month to learn UI and API automation tools such as Selenium and Rest Assured. But, it should be an ongoing process to upgrade continuously.
A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
Learn about different data structures – Arrays, Strings, Linked Lists, Stack, Queue, Heap, Hashing etc. Basics of search and sorting algorithms must be practiced.
Data structures are important to write optimized and scalable code. It helps us write code that would require less time to run. Learning to use which data structure for a particular problem is an important skill.
One can try to learn multiple programming languages, especially Java and Python. Do not focus only on syntax. Focus on the language paradigm, its weaknesses, and strengths. Understand where each language fits well.
Learning framework development using any programming language gives you exposure to different nuances of programming such as design patterns, project structure, code management.
Well written functional test cases save us from automating those test cases which are easy to automate but not effective in finding defects. Having good product knowledge is a prerequisite before starting automation. You should identify all test scenarios and test data before starting automation. Your test cases should be less dependent on other test cases. Identifying the right test cases for automation is important.
One should not automate everything. You can start with automating smoke tests first and then frequently performed tests, or where data driven testing with different test data could be used.
Automation testing does not replace functional testing. It is there to help functional testers focus on more important things than carry out repeated tasks. Functional testers should learn how to code and automation testers should learn how to test productively & efficiently.
Good luck on your learning journeys.
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