ROI of Switching to Codeless Test Automation: 3 Best Ways To Measure
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In today’s world, software teams are expected to ship features often, on time, and these features must be of good quality. Test automation with the right tools and built the right way will save countless hours of testing efforts, reduce defect escape rate, and improve efficiency and teamwork. But how do you measure that?
Suppose you already have some test automation in place but aren’t entirely happy with the efficiency and the outcomes. Let’s talk about the ROI (Return On Investment) of switching to codeless UI test automation – since it’s the most advanced type of test automation available to date.
- Based on saving the company from losing money due to bugs leaked to production
- Based on testing team staffing budget optimization
- Based on improved efficiency and reduced price of automated test creation and maintenance
Let us look into each one.
ROI of fewer bugs in production
The QA unit’s goal is to prevent issues in production because those issues would result in loss of revenue due to customer churn, inability for customers to purchase, or inability of the company to acquire new customers. The way you can approach it is to get “Dollar Escape Rate,”—meaning how much revenue is lost or not acquired due to issues in production. We created a detailed article some time ago detailing how to calculate Dollar Escape Rate here. To figure it out, you would need to partner with the CFO of your organization and get their help in estimating all of the events which resulted in losses or inability to acquire revenue related to the issues in production.
Once you figure out the amount of revenue A that the company missed during the last 12 months, you can divide it by the number of issues N that were caught in production during this time, and this would allow you to calculate the price of an issue:
P = A / N
By showing that you have reduced (or plan to reduce) the number of issues leaked to production by X percent, you can calculate the value of test automation:
Value = X * A
How will you reduce the number of issues leaked to production?
It is often impossible to achieve 100% test automation coverage with traditional test automation systems like Selenium. The main reasons are the slow speed of creating tests and a massive amount of test maintenance, as described in detail here. Luckily, with no code test automation tools like testRigor, you don’t have any of these two major issues. You can build new automated tests much faster, leading to a more robust coverage and fewer bugs escaping to your customers.
ROI of optimizing workforce
This is where codeless tools really shine. As described in detail in this article, the problem with traditional tools is that they consume a huge amount of resources on test maintenance – making it very hard or even impossible to cover all of the test cases. But don’t take our word for it – just look into this case of IDT before they migrated to testRigor. Or look into the case of Wine Access where test maintenance was a major drag for them with an old coded test automation tool they’ve used.
It goes like this: you are currently spending a lot of money on expensive QA Engineers, and their time is not used effectively, the speed of test creation is low, and it takes a lot of time to maintain the tests. By allowing your manual testers to be able to build automated tests 15 times faster than QA Engineers and spend drastically less time on test maintenance, you can relocate the QA Engineers to more appropriate projects like API testing. As time progresses and you quickly achieve your 100% of test automation with testRigor, spending almost no time on test maintenance, you can then transfer these Engineers to do Engineering rather than QA, thus reducing the overall QA budget.
Value = (Salary of a QA Automation Engineer) x (Number of QA Automation Engineers relocated)
ROI of improved efficiency
This one is quite an interesting twist on calculations. And it is based on the price of test creation and maintenance.
Let’s calculate the price of a single test. This is very straightforward: you can get all the yearly salaries of QA Automation Engineers and divide it by the total number of test cases automated during this 12-month period.
Price of a test = (All QA Automation Engineer salaries) / (Number of test cases automated last 12 months)
For example, you have 2 QA Automation Engineers, and you are paying $240K per year, and they automated 240 tests during the last 12 months. Therefore, the price of a test is $240,000 / 240 = $1,000 per test. Which is exactly the average price of a test with Selenium in 2022.
Now over the cause or the lifetime of the test Engineer spends, on average, 4.5 times more time maintaining the test compared to the time spent writing the test. A lengthy calculation of why it is 4.5 is provided here.
This makes an easy formula for test maintenance per test:
Price of maintaining a test = (Price of building a test) * 4.5
In our example, it would be $1,000 * 4.5 = $4,500 per test.
The total overall price of a test is, obviously, a sum of the price of building a test and the price of maintaining a test which comes down to:
Total price of a test = (Price of building a test) + (Price of maintaining a test)
In our example, the price of a test is $5,500 per test.
- Allows even Manual Testers (QA analysts) to build tests
- Allows to build tests 15X faster
- Requires up to 200X less time spent maintaining those tests
To calculate the ROI, let’s first estimate how many more tests we can build within 12 months: you can build 15X more tests with the same team, resulting in 240 * 15 = 3,600 tests. Let’s imagine that 3,600 is the number of test cases you have in total.
With the current (coded) setup, you might hope to get to this number within 15 years (3600 tests divided by 240 tests/yr), spending $240,000 * 15 = $3.6 Million.
If you can do it within one year, it will cost you $240,000 with the same resources. Therefore, resulting in savings of $3,600,000 – $240,000 = $3,36 Million.
On top of that, you’ll save 4.5 of that number of maintaining those tests. Resulting in additional $3,36 * 4.5 – 3,36 * 4.5 / 200= $15 Million in savings.
Impressive, isn’t it? This is how the ROI of switching to an excellent codeless test automation tool is calculated.
