We have a natural instinct to improve, automate, and make ourselves more efficient in our daily tasks. Take washing our clothes for an example. For years, washing clothes was a mundane task involving a washboard and hours of manual labour which rarely resulted in a high quality clean.
Today, we automate the process using a washing machine. We take five minutes to sort the clothes into piles, load in the soap, choose the program, set it to start and are then free to perform other tasks while the washing is going round.
Now, let’s apply this principle to testing. Automating tests minimises the risk of human error due to repetition, allows for out of hours executions and frees up testers for the necessary manual tests. Automation speeds up the testing process, helps to ensure quality and, as a result of shorter test cycles, cost is reduced.
With up to 40% of the entire software development lifecycle spent on testing it is clear that there are efficiencies to be made here. Automation of test cases helps to support the business earlier without having to compromise on quality.
Man vs Tool
Automated testing is not designed to replace manual testing, and sometimes manual tests are necessary. However, automation is intended to improve testing by removing bottlenecks that are created by those unnecessary manual tests that take time and are prone to error.
Bottlenecks caused by manual testing are going to seriously slow a project down and as a result cause late delivery. The manual process of creating test data from scratch is one of the most labour intensive and mundane tasks in testing. It is also a process that rarely results in the creation of quality data that is fit for purpose, causing poor test coverage and bugs in production.
Auto-creating test data using a data generation tool will enable testers to have the exact data they need to satisfy their test cases, when they need it. They will avoid having to spend hours manually creating test data, or waiting for data to flow from downstream teams. Removing this data roadblock frees up testers to perform other important tasks that will ensure a quality, well tested system. Data that is fit for purpose and has the ability to test outlier data conditions (an ability which production data doesn’t have) will thus enable teams to rigorously test a system’s existing and future functionality.
So while manual testing has its place, so does automation. By automating your test data creation you will have fit for purpose data delivered to your system when you need it. Auto-creating data from requirements will also ensure that not only do you get your data quickly, you have the exact data required to satisfy your tests.
Find out more from this webinar: “Quality Data for Quality Testing”