Test automation plays a crucial role in today’s fast-paced software development world. It enables companies to release their products faster by improving efficiency, speeding up the test cycle, and detecting bugs early.
But how does one measure the effectiveness of test automation? What calculations are needed to check whether the test automation solution gives optimal returns?
One such metric that is often used is called test coverage. It’s a measure of test quality that reveals how much of the software has been tested. This particular measure gives a quantitative measure of the test’s extent. It also tells us which aspects of the software have been sufficiently tested and which parts need more work. Although, one also needs to be careful not to confuse the quantitative measurements of test coverage with the qualitative ones.
Despite its limitations, test coverage is a critical metric that helps put a number on the effectiveness of test automation.
So, let’s first understand what test coverage is.
What is test coverage?
Test coverage measures how many aspects of the software have been tested. It’s most effectively used to detect sufficiently tested components, the quality of the tests being meted out, and what parts require more testing. It also covers test requirements, codes, user combinations, user scenarios, and user platforms.
Test coverage is measured using specific metrics. These metrics define the code lines covered under tests, the number of bugs found, and the time taken to do so. By analyzing these metrics, testers can fine-tune their testing strategy and improve efficiency.
Some of the most commonly used test coverage metrics are:
● Test execution coverage percentage
● Requirements coverage
● Test cases by requirement
● Defects per requirement
● Defects per platform under test
● Defects per functional area
● Test case performance in finding defects
Why is test coverage essential?
Test coverage measurement has numerous benefits that make it a crucial part of your test automation strategy.
● Reduces the number of bugs and defects
Test coverage doesn’t just measure the number of bugs found. It also gives us an idea of the bugs that weren’t found. Together, they help keep track of the bugs in future software iterations.
● Helps implement different types of tests
Test coverage measures and reports the aspects of the software that require more rigorous testing.
● Build long term efficiency in testing strategy
Keeping track of the test coverage metrics enables testers and automation engineers to build better digital test labs. It also helps them pinpoint which test scenarios to include in the tests and which phase of the cycle to implement them.
What are the different techniques used to measure test coverage?
We’ll be covering three of the most commonly used methods to measure test coverage.
Code coverage
The most popular metric, code coverage, essentially measures the number of code lines the tests cover. It reports the total number of lines in the code and the number of lines tested by the test script. This category consists of the degree to which the source code of a program gets executed. Higher code coverage drastically reduces the chances of having undetected bugs slip through. Often, testers break down the measurement into different levels, such as branches and decisions inside logic constructors.
Data-oriented coverage
In this category, the input and output parameters are compared. Each parameter has a spectrum of values it can hold. Each possible combination of the values is supposed to be tested. But that can often prove to be cumbersome. Many testers, instead, go for something called the ‘each choice’ coverage. This practically means that each possible value is chosen once. Then there’s another method called the ‘all-pairs’ method. It’s a combination of both the methods mentioned above and has the best ROI.
Other kinds of coverage
Apart from the above-given types, there are other less-frequently used methods too. These include coverage of state machines, decision tables, decision trees, equivalence partition, and boundary values.
Then there’s coverage that isn’t related to the code at all. Mobile fragmentation is one such example. It tells us how many combinations of mobile devices, OSs, screen sizes, resolutions, and browsers have been covered.
Maximizing test coverage is essential to building an excellent software product. Using a robust test automation solution can help you achieve just that. Avo Assure, one of the leading test automation solutions globally, requires no coding and comes with a pre-built keyword library. Its easy-to-use UI and interactive reporting make test automation easy for even non-technical personnel. Avo Assure’s robust software helped a large Fortune 500 Bank attain 100% test coverage with 63% cost savings.
Book a demo today to learn more about how Avo Assure helps expand test coverage efficiently!
