Test Coverage – What it is & Improve it Using Testsigma

To answer the below questions and let the testers know their test quality, we invented the concept of test coverage.

The software release goes through two main phases – development and testing. When the software is under development, a developer codes the required features and lets the team know he is done with it. How do we verify this? By using the functionality on any of the systems. It is then cleared for testing. Now when the software is under testing, a tester tests the functionalities of the application and lets the team know he is done with it. But how do we verify this? We cannot rely on the feature list as in development. What about that one scenario in the latest module that never got tested? Or some functionality that got tested twice? We may never know.

The test coverage of an application provides a clear answer to what functionality is tested and in the end, summarises the analysis with respect to the complete application. But this is not as simple as it looks. With embedded sub-concepts and complex observation of results, test coverage is much more than a percentage number (which can be misleading sometimes). Decluttering this in the post, we will try to explore the corners of test coverage and see if we can elevate our numbers with the help of Testsigma.

What is Test Coverage?

When we mention the word “test coverage”, we are defining the functionalities and requirements that have been covered with our test cases. So if our test coverage number is 50%, we are essentially saying that our tests executed on only half of all the functionalities, modules, and requirements in the application. This is one of the popular metrics used to define the test quality and whether our tests are actually testing the application or just giving the impression that they are. In the testing world, people will often confuse test coverage with code coverage and use them interchangeably. These two concepts may look familiar as their internal working is almost similar, but they do set apart from each other in their foundation. A detailed guide on code coverage vs. test coverage will help you explore this area further.

Types of Test Coverage

When it comes to qualitative measurement of your application’s functionality – various aspects form it together. From functional requirements to system requirements and risk minimization, here are the different types of test coverage:

• Requirement coverage: The most important test coverage is requirement coverage and it aims at catering to all the user requirements. 
• Risk coverage: It is centred around identifying things that can potentially be risky for the software application and the users. Risk coverage is entirely dependent on the type of application you are developing.
• Product coverage: It comprehensively covers the product and validates the software product’s strength to withstand complex situations. The core objective is to test if the product works fine in extreme conditions too
• Boundary value coverage: Can you recall the boundary value analysis in testing? This type of test coverage verifies the system for boundary values in numerous inputs. Ideally, the boundary value coverage should be combined with other types of test coverage as the last thing you would want is your software accepting figures it strictly should not.

How to Perform Test Coverage?

It can be executed by leveraging different types of testing. However, businesses have varied requirements and hence which test should be run for this depends on their specific requirements. Some of the most popular techniques to perform are

1. Unit Testing: Each component referred to as a “unit” is tested separately. This approach makes it easier to find and rectify errors. 
2. Functional Testing: All the features of an application are tested by referring to the FRS (Functional Requirements Specification) document.
3. Integration Testing: After all the modules or units of the application software are tested individually and integrated, integration testing is performed. The system is tested as a whole.
4. Acceptance Testing: Also known as user acceptance testing, this approach is used to verify whether a software application is ready to be shipped to the end-user or not. 

Benefits of Test Coverage in Software Testing

If we could summarise the top list of benefits , we would get the following points:

Early bug detection

Various types of test coverage techniques aim at focusing on various different angles of an application. This helps in digging deep into each corner and exploring bugs that could have escaped the testing phase and hence saving the overall costs.

Exploring gaps in the testing phase

Test coverage aims at covering maximum application code, modules, functionalities, and requirements through test cases. If we get a lower test coverage number, we know that the application is not covered completely even if it may look like that due to the number of test cases. This explores major bugs in the testing phase and helps modify and restrategize our test case mechanism.

Increased ROI

When our test cases are of high quality and we spend less on maintenance and debugging, we ultimately save a lot of money in the long run. This increases the return on investment, a KPI that every organization loves.

Removes dead code

Sometimes a feature is removed from the application but its test cases remain in the pipeline. Since test coverage generates the percentage of coverage on the current source code, it is extremely easy to identify the test cases that are dead for now which was harder to identify manually.

In addition to these major points, we should always consider the smaller changes that test coverage brings with it such as streamlining test cases, defining the impact of tests on the source code, keeping costs in control, etc.

Test Coverage Characteristics Using Statement Test Coverage

1. if (age < 5): 2. cat = “Infant” 3. elif (age > 4 and age < 13): 4. cat = “Child” 5. elif (age > 12 and age < 19): 6. cat = “Teenager” 7. else: 8. cat = “Adult” 9. return cat

Our application just prints out the category of a person based on their age. Now, I have two test cases as follows:

Test case 1 :

def test_if_teenager: return_val = module.func_name(18) self.assertEquals(return_val, “Teenager”)

Test case 2:

def test_if_infant: return_val = module.func_name(2) self.assertEquals(return_val, “Infant”)

The first test case covers lines 1, 3, 5, 6, and 9 while the second test case covers lines 1,2 and 9. So in total line numbers 4,7 and 8 were never executed. This results in (6/9)*100 = 66% statement coverage. As a manager, we cannot go over each line and mark manually which lines are executed. There may be more than 100 thousand lines of code running the test cases. The statement coverage helps us understand if there is a requirement to jot down more test cases to cover more of the application. This scenario is popularly termed “improved test quality” and is a characteristic of all types of test coverage.

Secondly, the number of tests (if used as criteria) can give a wrong perception of tests. If I change test case 2 above as:

def test_if_teenager_new: return_val = module.func_name(16) self.assertEquals(return_val, “Teenager”)

Notice that it is a different test case with a different value passed into the function, but it will execute exactly the same lines from the source code that got executed in test case 1. With even two test cases, our statement test coverage has been (5/9)*100 = 55%. Test coverage uncovers such misleading scenarios and highlights the need for new test cases that could execute newer lines. This scenario is also called “eliminating the redundancy”.

Lastly, when we are able to achieve both the scenarios mentioned above, we reduce the bugs that could be reported by our customers. Therefore, we save a lot of time and costs on the project and the organization’s budget. This scenario is called “saving time and money”.Apart from this, other benefits such as reducing bugs, improving app quality, etc. can be deduced from these scenarios.

Test Coverage Measurement Methods

Measuring test coverage is not as simple as measuring code coverage because of the qualitative nature of test coverage. Numerous techniques exist to evaluate the test coverage based on the application and industry. Although a majority of test coverage measurement methods are very specific to certain relevant cases and cannot be used out of scope, some popular ones can be applied to every project:

1. By functionality: This technique involves linearly verifying if all the features of your application are being validated by tests. You just need to create a list of all the functionalities of the application and start evaluating them one by one. Even though this method doesn’t throw light on how well those features are tested, it definitely does the hard check on the number of validated functionalities.
2. Test code coverage: One step ahead of the first method, this test coverage technique uses a dedicated tool to return the percentage of code that got implemented while testing, which is mostly automated. Again, in this case, the execution of code doesn’t guarantee the correct results for any of the tests. However, information about how much of the system code got executed helps in determining the test coverage and optimizing it further.
3. By User Interface: The primary aspect which is directly related to the end-users and hence needs to be thoroughly examined is the UI of your application. So UI elements of your application like frames, navbars, pop-ups, buttons, drop-downs, scroll bars and others need to be considered by your tests and all the interactive actions lead to desired results through the user interface.
4. By journeys: Each possible path that a user can follow to get the desired results through your application is a journey. Every URL of your platform along with buttons, internal links and menus leads to a result and hence should be comprehensively tested.

How to Achieve Test Coverage?

The formula for calculating this is simple to implement but the absolute value of the variables is harder to calculate. Modern editors are capable of calculating the total lines of source code but how many lines got executed by our tests is still something that is quite impossible to apprehend. Here, we consider that modern applications have source codes of more than 100,000 lines and a lot of the times more than 1 million.

This, however, is just about statement coverage. A technique for which there are a lot of tools available online. Personally, I write in IntelliJ which provides in-built functionality for this purpose. IntelliJ can calculate test code coverage with just a single button click. If you are using any other editor, it is better to check for this functionality first.

For other types of test coverage techniques, we are not so lucky yet. Those techniques can be either performed manually, partially through automation, by inspection through peer reviews, or by functional tools that do not cover the entire area. These options do give us a boost but may not travel with us all the way till the end.

How to Calculate Test Coverage

To isolate the theory and calculation mixed a bit in the above sections, we can create a generalized method of calculating the test coverage in software testing. The formula will get changed with the type of test coverage involved. For simplicity, we will consider statement coverage here.

So if we take the above scenario, we had 9 lines of source code in the application and the test case could execute only 6 out of them. This means per line, the test case made only 6/9 of the code execution or in other words covered in the source code. This can be generalized as:

No. of lines executed in the source code / No. of total lines in the source code

This is, however, just a probabilistic analysis in the range of 0 to 1. For easier and simpler understanding, it can be converted to percentages by multiplying by 100. So our percentage formula becomes:

(No. of lines executed in the source code / No. of total lines in the source code) * 100

Putting this formula in our scenarios above, we get a percentage test coverage of 66%. As a standard across the globe, percentage terms are used against the probabilistic and this will be the standard throughout this post as well.

Tools to Perform Test Coverage 

• PyUnit 

The tool is primarily designed for unit testing and is one of the most popular test coverage solutions. A Python-based framework, PyUnit ensures smooth development of test cases, tests, suites and test fixtures. 

• JUnit

It is an open-source unit testing framework compatible with Java. JUnit is utilized by developers to deliver mundane tasks like writing, executing and analyzing tests. Interestingly, the tool is powerful enough to cater to user interface and regression testing synonyms. 

How to Improve Test Coverage?

It is an important metric, and testers work hard to improve its number as much as possible. While some things may depend on the type of project we are working on, the following simple steps may help facilitate things further:

Never start without planning

Setting on an adventure without proper planning has been a matter of concern in not only test coverage but another testing phenomenon as well. The general problem we face when we do not plan is that we often realize we are going in the wrong direction after we have started the process. This leads to either change in strategy at that point or starting from the beginning again which are both expensive processes.

When we talk specifically about test coverage in testing, we are taking into consideration a wide variety of tests such as cross-browser tests or functional tests, etc. When there are so many individual elements of a certain testing type, there is a higher chance of things going wrong. A good practice to improve test coverage is, therefore, always planning each step before execution.

Keep removing redundant code

As the application grows vertically and your team inducts more members, there will be more test cases that will focus on a single domain or functionality. When such a situation occurs, remember that we are not increasing the test coverage but just re-executing old code with newer modifications. Any unused code should be removed constantly with time. This will also reduce the size of the tests and increase quality.

Use cloud solutions as much as possible

The huge size of an application that gets released today is hard to analyze on the local system. There are multiple issues when we do the work on-premise. Firstly, we are dedicating one system for testing and saving all our work on it. What if that system breaks down one day? We also commit too many system resources for the tools and plugins we use. Since today’s testing cannot be done by a single framework or tool, we need to dedicate all our resources to these smaller units which slows down the process.

Finally, on-premise systems cannot be used when we need to implement test coverage on multiple operating systems, devices, and browsers. Thankfully, today, we have tools that help us not worry about these smaller issues and focus more on the testing part. One such tool is Testsigma.

Improving test coverage with Testsigma

Testsigma is a cloud-based test automation tool that provides all the modern test techniques without downloading anything to your local system. For someone who is looking to expand and scale their test automation cycles to increase test coverage on an ever-increasing software size, Testsigma seems to be the best available option. The platform comes with more than 3000 screens and real devices on their system that can facilitate compatibility coverage and cross-browser testing with ease.

With device-specific test automation tools like mobile test recorders, Testsigma provides a complete ecosystem of software that comes integrated with third-party plugins, tools, and software. However, this is not even the best part. Testsigma is not a conventional framework that asks you to learn a programming language to be able to use its functionality. The platform’s medium of test script formation is the English language which uses natural language processing to convert it to tests behind the hood. As a tester, all we need to do is write English sentences and see our tests run magically on the device of our choice.

All this comes free of cost at Testsigma’s platform and a sign-up is all it takes to start our journey.

Inducting Test Automation for Test Coverage Enhancement

Another important step to increase this in software testing is to induct test automation in the process. While this section should be a part of the previous section, it brings so much value with it that keeping it isolated would make things much clearer.

Automation is a process of automating the stuff we previously were doing manually. When it is done in the testing, it is called test automation and when it is done in test coverage, it is called “automation test coverage”.Automation test coverage uses tools and frameworks that take instructions from the user and runs them repeatedly on their own. This would mean that the test cases we have written to be run manually would now be needed to be transformed into automation scripts instead. But as we know, 100% automation is not desirable and a lot of the time not even achievable in the testing phase. So, test automation coverage needs careful assessment of all the test cases and converting only those that will be reused heavily in the long run and will provide a return on investment. This makes the test automation coverage as:

Test automation coverage = No. of tests automated / No. of total tests

So if the team converts half the test cases to automation, they achieve a 50% test automation coverage.

Other than this, the choice of automation tools and their working remains similar to normal test automation and contributes nothing as such in the test coverage area.

Can Testsigma help in test automation coverage?

Test automation tools just focus on executing the scripts and applying their modules to make this process smooth and efficient. They cannot write unique test cases for you and increase the test coverage automatically. However, tools like Testsigma can make the automation test writing process easier, and that in turn can help facilitate the test automation coverage.

When a tester opts for Testsigma, they write test cases in plain English which is much faster and easy to debug. As a result, they save a ton on maintenance in the future and make complex cases look extremely simple to understand.

Another feature of Testsigma is a mobile test recorder. It can record test cases while the tester interacts with the application on the platform. If the tester believes that a certain action from the end user should be converted to a test, they can mimic the same action and the test will be ready to add to the suite.

Is Test Coverage a Correct Metric to Understand Quality?

Before we dive deeper into this, let’s answer the question of the correctness of test coverage. Let’s say I, as a tester, and my teammate, also a tester, decide to write separate test cases on the same source code. My test coverage turns out to be 70% while my team member’s comes to be 90%. Can we declare then and there whose tests are better and proceed with my team member’s?

This is something that looks simple at first glance but computer scientists, testers, and language experts have always been in debate about this question. The problem is with the over obsessiveness of increasing the test coverage in software testing. Software testers know that to glorify their tests, they need high test coverage and due to this they make test cases over complex. This puts a huge dent in maintenance costs and understanding these cases by people other than the author becomes very hard.

As a thumb rule of working with test coverage, always remember that higher test coverage does not mean a better test suite. Contradictly, probably it would be an expensive deal and should always be avoided. It will save both time and costs for the company.

Limitations of Working with Test Coverage

Test coverage may be one of the most important metrics to analyze the depth of testing in accordance with the application’s requirements and source code. However, test coverage is not that easy and therefore it creates small gaps in the overall process.

• Unavailability of tools: If we just talk about product coverage, there needs to be a tool that can take functionality and determine what tests are based on it. Tools working with such highly complex problems are not available.

• Lack of automation: As focused on a couple of times in this post, automation tools do not work with test coverage as there doesn’t lie any bridge to connect these two. Hence, there lies a lot of manual work which makes things slower.

• Adds delays: Since test coverage needs to be analyzed manually, it takes a lot of time to complete the process. Being an important aspect of the testing phase, it cannot be skipped, and hence the consequences are put down heavily on the deadlines.

• Never know what we are missing: The test coverage of an application cannot be achieved as 100%. It just makes things a lot more complex which has a price to be paid in the future. But this creates a gap in the process. We may have to leave a few things at risk hoping that there are no bugs inside that part of that code. Since we can never be sure, it is a risk that comes with the test coverage.

Test Coverage Best Practices

Best practices include,

• Assessing the risk areas – Any area that is prone to failing and is user critical should be tested well.
• Reaching for almost 80% test coverage measurement – Write test cases that cover at least 80% of application features.
• Give priority to user requirements – Consider the functional and user requirements to implement the requirement coverage technique.
• Be smart during test case creation – use test coverage techniques that validate the right scenarios and cover the maximum amount of code.
• employing the right automation tools for testing – go for test automation tools that allow you to be flexible with your test automation approach and let you apply the test coverage techniques we discussed.

Conclusion

Test coverage is one of the most important metrics in the testing phase. It helps analyze the quality of tests, testing as a complete part, and exploring gaps in the system. While it is sometimes confused with code coverage (which is similar to statement coverage), it is not that simple and straightforward.

Calculating test coverage with respect to the functionalities, requirements and other product specifications is tricky and a bit complex. In this post, we tried to unwrap the layers of test coverage and discovered its types that help focus on different areas of an application.

Analyzing its types, improving methods, and mixing with automation, we can easily say that while test coverage is not a parameter for judging the quality, it definitely is one of the most interesting pieces of this lot. With this, we hope this post helps you cover maximum code, branch, and other requirements in the next projects. Thank you for giving this post your valuable time.

Frequently Asked Questions

How do you measure test coverage?

Measuring this is a bit trickier and may depend on the type of test coverage you are working on. The simplest of all, statement test coverage, for example, considers the number of statements covered in the test cases. It can be measured by :

no. of source code lines covered / total no. of lines in the source code

What is good test coverage?

Test coverage is always aimed at 100%. However, due to unwanted complexities in the test cases, it is rare that a team can achieve a number as high as that without side effects. Generally, there is no criteria to categorize a test coverage matrix as “good” or “bad”. Practically, anything above 75% is a good job considering the average of projects. However, there are no such standards.

How to adopt a proper Test Coverage method?

All methods described in this post are required to achieve good overall test coverage. They provide a good mix of tests that will help you find glitches in various parts of code. As a tester, you can give one type more priority than the other based on the project requirements. But practicing all of them will yield good results.