When companies consider embracing test automation, one of the first things they consider is lowering testing costs. After all, software testing is prohibitively expensive, accounting for between 23 and 35 percent of total IT spending, according to the 2017 World Quality Report. Automating testing allows you to focus on more interesting and value-added tasks while still executing regular checks faster, more frequently, and more precisely.

Clearly, there is a market for it. Even open-source testing tools, however, necessitate a financial investment, which necessitates the approval of a higher-up. How do you persuade management that test automation is worth the work, time, and money?

The most common method is to calculate the return on investment (ROI) as follows: Calculate how many manual testing hours you can save, then multiply it by the hourly rates of the testers. That is unquestionably a wonderful place to start. Time-to-market, quality/risk, and a much broader assessment of ROI are all parts of a compelling business case for test automation.

1. Time to market

Time to market refers to how quickly a new feature or upgrade can reach the end-user. The longer it takes to introduce new features, the less adaptable you’ll be, and the more difficult it will be to respond to changing client demands and market trends. Delays can have a negative impact on income, operational efficiency, and/or your competitive advantage, depending on the nature of the application.

Given that testing is frequently listed as the leading cause of delivery bottlenecks, it appears reasonable to conclude that testing efficiency has a significant impact on time to market (how fast you can execute your tests).

In turn, testing efficiency is measured by the time it takes to run an automated test (including preparation, execution, and analysis) vs a manual test (again, including preparation, execution, and analysis).

The larger the gap, the better your chances of achieving time-to-market benefits. The bigger the payoff, the more frequently you perform tests.

Other factors that influence the effectiveness of testing include:

  • How many tests you should run: This could be more or less than you’re currently running—test case design approaches and risk assessments can help you figure this out.
  • Test data and test environments are both available: If you want to get to the finish line faster, it’s not enough to just define automated tests; you also need constant access to all of the parts needed to execute them.
  • The point in the software delivery lifecycle where testing takes place: “Shift left” tactics like automated API testing and employing new technologies to start testing UIs and APIs before they are developed can improve testing efficiency and speed up time to market.

2. Risk reduction

The goal of risk reduction is to reduce the number of errors that make it into production. Defects that get loose cause a slew of problems. You’ve probably seen the graph that shows how patching bugs after they’ve been released is exponentially more expensive than catching them early on. However, there is also the issue of downtime, support expenses, and, in many businesses, penalties to consider.

It’s crucial to understand your company’s risk coverage while analyzing risk reduction. Ranking the risk of your requirements or use cases against one another, mapping tests to those risk-weighted requirements, and evaluating how well your testing covers your top risks are all part of determining this.

It’s entirely possible for ten strategically designed tests to cover far more risk than 100 tests built without regard for risk. The smaller the defect density, the larger the risk coverage.

3. ROI

The third element is the one that most people consider first: How much time and money can you save by testing more efficiently? You may run more tests in the same amount of time—or the same number of tests in less time—with the help of automation.

You can find a lot of quick-and-dirty formulas online, but doing it right depends on a few things. Don’t forget to think about things like:

  • Number of test cases
  • Number of releases
  • The time and effort required to manually create reports
  • The number and types of applications you’re testing
  • The duration of manual test execution
  • The time required to acquire/create and prepare test data
  • The time required to acquire/create and prepare test environments
  • The test suite’s business risk coverage
  • The test suite’s level of redundancy
  • Who is involved in testing (including business experts) and their daily rates
  • The total cost of labor
  • The cost of fixing a defect that slips into production
  • The stages of your tests (smoke vs. daily vs. full regression)

Putting the pieces together

Here’s a real-life, anonymized example from a large European food and beverage industry to give you a tangible notion of what a business case accounting for these many parts might look like.

All of the data points are important, but let’s focus on the most important ones:

  • The current risk coverage for the company is 45 percent. Most firms that are just beginning their testing transformation have risk coverage of 45 to 55 percent, which is fairly common. Many businesses fail to calculate risk coverage. This company hasn’t yet optimized its test suite or taken a systematic approach to test case creation (pairwise, orthogonal, linear expansion, etc.).
  • Only one application is being tested, although it is a major one: SAP ERP Central Component (ECC). Test automation for complicated business apps that require specific subject expertise pays out more than test automation for simple web or mobile apps, for example.
  • The total number of flaws discovered in the test was 650.
  • The company delivers one major release and eleven minor releases per year, as well as 48 patches. To guarantee that fundamental business processes are not disturbed, they all require comprehensive testing.
  • A production error costs €3,500 (about $4,200). This comprises the cost of repair as well as the cost of downtime to the company.
  • For this team testing a single SAP instance, the total cost of testing was €1.4 million (about $1.7 million).
  • Although this company is experiencing a digital revolution, all testing is done by hand. Testing, predictably, is a stumbling block to the company’s innovation speed. It has adopted agile and is working in Scrums, but testing is still a few sprints behind. It wants to increase the frequency of its releases, but testing is preventing it.

If you look at this business case solely in terms of cost savings, you’ll notice that the cost of testing actually rises slightly. However, the risk reduction and cost avoidance gains are substantial, ranging from €2.3 million to €19.8 million.

Once the organization transfers a major amount of manual testing to test automation, delivery is estimated to be nine times faster (see figure below). Because of the increased testing frequency, nearly ten times more flaws will be discovered in tests (from 650 to 5,649).

Take it to the next level

You can move on to the next step if you have a clear picture of how test automation will pay off in terms of speed, cost, and risk. What is it that your company is attempting to achieve right now? Is it bringing innovation to market faster? Increasing the efficiency of your company? Is it possible to improve digital client engagement and experience?

Explain how your test automation initiative affects these high-level company goals, and you’ll find that getting management approval is a lot easier. Then comes the next challenge: aligning your testing strategy with these objectives and reporting on how test automation is helping to achieve key business objectives.

For more info: https://mammoth-ai.com/testing-services/

Also Read: https://www.guru99.com/software-testing.html