| 17 Jul 2020
If we asked someone from the 19th century about a better means of transportation, they would have probably asked us to find a faster horse. But now, we are talking about autonomous vehicles, self-driving cars, flying taxis, and even space-ships. No matter how comfortable we think we are now, there will always be ambitious alternatives directing us towards a better and a more comfortable future. Every industrial revolution was a paradigm shift in the way things worked until then, directing us to an advanced future. If the third industrial revolution was all about the internet, the fourth industrial revolution is all about automation and artificial intelligence. If coding was a new technology a few decades ago, codeless automation is now the new technology.
Many codeless tools/ frameworks are now taking over software product development, and QA & testing is not an exception to this movement. CI/CD, continuous testing, and agile practices have changed the way QA works and helped teams release more often, efficiently, and faster than before.
These test automation frameworks are typically platforms designed byintegrating a set of rules and instructions with multiple software libraries and test automation tools to expand their ability to provide a bigger solution of faster and better test assurance. These frameworks help in making the testing processes more efficient and reduce dependency on the developers and automation engineers, thereby improving accessibility.
Over the years, the test automation frameworks have evolved significantly and most of the frameworks that we see in the current market characteristically fall under one/ more of these archetypes:
Linear scripting framework:
This is one of the fastest ways to create test scripts and record them. Automation skill is not required and the scripts are not reusable. Any changes to an application will need a lot of rework and maintenance.
Modular based testing framework:
Any changes made to the application will affect only the corresponding module and hence, only the individual test script needs to be reworked. It cannot use multiple data sets and is programming heavy.
Data-driven testing framework:
Multiple data sets can be used and multiple scenarios can be tested by making changes to the data. However, this framework needs high programming knowledge and takes a significant amount of time to set up.
Needs minimum scripting knowledge, code is reusable, and test scripts are modular. Initial installation costs are high, but maintenance is easier. There is a flexibility to add/ modify keywords as per business/ testing needs. Needs good automation awareness.
Test-driven development framework:
It uses modular, reusable code, and requires users to write unit tests before writing implementation code. Hence, refactoring code is easier to implement. Defect probability is low since development is test-driven. Tests beyond unit tests are hard to write and writing good tests necessitates the skill of manual testers. It is difficult to apply to existing legacy code and maintenance is difficult.
All the frameworks have a few characteristics that are common to them, albeit, in different ranges and they can be listed as below:
Common challenges in adopting automation or automation frameworks
According to a study by the University of London, only 38% of global workers use automation technology in their everyday roles, and only 3% of CEOs said that they have implemented automation at a “fundamentally operational” scale across their companies. Few reasons at a root-level may be listed as:
Organizational changes: Implementation of automation needs changes at an organizational level. The tools and the test automation approach needs to fit well with the existing business and processes without impacting the business in an undesirable manner.
Training & Up-skilling: Companies must up-skill and re-skill their current employees to work on the tools and help them adapt to the new tool. This understandably takes an investment of both time and budget.
Identifying the right KPIs and reporting: Goals and KPIs need to be revised to suit the new testing approach and the new tools.
Maintenance: The conventional script-based automation tests need frequent updating to keep pace with the dynamic environment. This results in unnecessary overheads due to heavy maintenance efforts.
Lock-in: If the framework is not flexible and follows legacy infrastructure and data systems, it may result in a vendor lock-in which is not desirable to most of the clients
The current market gap:
ValueLabs Test automation framework:
A component of the ValueLabs top-of-form quality assurance engineering suite, the ValueLabs TAF is a next-gen hybrid test automation framework that comprises of a combination of best practices and tools that are designed to help QA professionals test more efficiently.
ValueLabs TAF solution:
ValueLabs TAF is an end-to-end automation framework that helps:
How is ValueLabs TAF different?
When looking for a suitable test automation framework for your company, you have to make sure you find a tool that is quickly and easily adaptable to your testing and business processes. The tool needs to be flexible and should be able to support a wide range of applications and processes. This approach will help you in making your QA team efficient and best-in-class.
By leveraging TAF, one of our customers, an American entertainment company has been saving 40-60% of regression effort per cycle. Another customer, an American airlines giant has increased productivity by 30% and TAF has been helping them generate 10,000 records of complex data every day.
Contact us to understand how we helped them and other clients achieve their quality and automation goals with our Test automation framework (TAF).
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