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Top Test Coverage techniques in Software testing

A CISQ analysis estimates that enterprises in the US lose $2.08 trillion annually due to subpar software. Software tools are essential for many types of businesses and industries, yet they can have serious defects and performance problems. Software flaws can no longer be found and eliminated by human manual testing.


To increase the overall caliber of their software testing, product or software development organizations must switch to automation testing. The main query, though, is: What proportion of the application code needs to be tested? Does our product need to be tested in its entirety? Or should companies give top priority to the crucial areas most likely to result in issues?

The software testing parameter known as “test coverage” determines how much testing a product must undergo.


What is Test Coverage?

The technique used to calculate the percentage of test cases that cover the complete program code is called est coverage. When the test cases are executed, it can decide what degree of code testing to perform.


Take an application with 5,000 lines of code, for instance. Test coverage is 50% if the specified test cases are able to test 2,500 lines of code.


What distinguishes code coverage from test coverage? The percentage of code lines that are tested with at least one test case is known as code coverage in unit testing. Test coverage is essentially a gauge of the effectiveness and usability of the entire program.


Eight Methods for Test Coverage


  1. Product Information

The different components or modules of the tested application are measured using the Product Test Coverage approach.


For instance, testing the fundamental add/remove item functionality and scenarios like as managing a cart with a maximum number of items or the cart’s behavior when an item is out of stock would be included in the product coverage of a “shopping cart” application.


  1. Insurance Against Risk

Finding and thoroughly testing the risk aspects (in any application) is the main goal of this test coverage technique.


An example of a risk aspect in an eCommerce app is the ability of users to successfully connect to and complete payments through a third-party payment gateway. The risk evaluations indicate that four possible outcomes are likely to occur, specifically:


“Likely scenario with High impact” necessitates testing by law.

The “Unlikely scenario with High impact” has to be tested as well.

Testing is only necessary for the “Likely scenario with Low impact” if time permits.

“Low impact, unlikely scenario” – does not need to be tested.


  1. Conditions Reportage

The product or application’s compliance with the customer’s requirements is tested using the requirements test coverage technique. According to this method, “high-quality products are useless if they don’t serve user requirements.” This method basically assesses if the finished product satisfies the customer’s stated needs and has all the “promised” features.


For instance, requirements coverage would check if, as stated in the app’s feature list, customers may book flights with many stopovers if a client’s demand for a travel booking app contains that functionality.


  1. Coverage for Compatibility

Coverage of compatibility tests guarantees that the final application is tested on all supported browsers and devices. Consequently, the goal of this strategy is to lessen compatibility-related issues with the product.


As an illustration, test a website on several browsers (such as Chrome, Firefox, and Safari) and hardware (such as PCs, tablets, and smartphones) to make sure that the functionality and layout remain the same no matter how users visit the site.


  1. Coverage of Branches

Branch coverage guarantees that your code is tested at least once along every potential path. It is essential for finding hidden faults and guaranteeing that every code branch is error-free.


For instance, in a weather forecasting app, branch coverage might test both cases where a temperature threshold is met, resulting in ‘Hot’ or ‘Cold’ depending on the temperature, and one when the threshold is not met, resulting in ‘Hot’.


  1. Coverage of Codes

Code coverage calculates the proportion of your code that has been tested, which aids in locating untested sections. It’s a crucial indicator of how much testing you’ve done.


Code coverage, for instance, would entail writing tests that exercise each of the addition, subtraction, multiplication, and division operations in a calculator app to make sure all the code is tested.


  1. Coverage of Statements

Statement coverage verifies that each line of code is run through the testing process. This method is crucial for finding flaws and guaranteeing thorough code testing.


Statement coverage, for instance, would guarantee that each feature (such as text formatting, picture insertion, and page layout) is tested at least once to make sure all lines of code are run during testing for a document editing tool.


  1. Coverage of Boundary Values

Testing the outermost boundaries of input ranges is the main goal of boundary values coverage. Errors at the boundaries of input conditions must be detected in order to guarantee reliable data treatment.


Example: To guarantee appropriate handling of edge situations in an online form that takes age input, boundary value coverage would test the limits by inputting the maximum and minimum allowable ages as well as invalid ages (such as negative integers or abnormally high ages).


How Is Test Coverage Measured?

Finding out what percentage of the code in your application has been tested is the first step in measuring test coverage. This measure is essential to comprehending how successful your testing approach is.


Test Coverage is calculated as (Number of Code Lines Tested / Total Code Lines) × 100%.


You can easily see how much of your codebase is covered by tests by using this formula, which gives you the test coverage %.


Advantages of Test Coverage

Regardless of the method employed, test coverage offers application development businesses numerous advantages. Below are a handful of them:


  • Companies can save time and effort by implementing test coverage to identify defects early in the development process.
  • Test coverage can reveal areas where customer needs aren’t being met, which promotes product adoption and improves customer satisfaction.
  • Test coverage enhances the entire testing strategy by identifying “what needs to be improved” in the application.
  • Less faults during user acceptability and production testing result from efficient test coverage, which reduces the amount of resources used.
  • Test coverage can raise automation testing’s return on investment through tracking and quality metrics.


Why Is Test Coverage Required for Test Automation?


Development teams can test more features of their products by increasing the quality of test coverage in software testing with the help of Automation test with selenium. QA engineers can devote more time to developing high-caliber test cases because of automated test cases. Test automation is another tool that QA teams can employ to guarantee test coverage during all stages of development. This makes it possible to identify and fix product problems early on (before they cascade into critical flaws).


Furthermore, test automation offers advantages such as:

  1. enhanced test coverage, encompassing the automated test cases’ domain.
  2. Reusability of current test cases for additional use cases, often known as replication.
  3. Real-time analysis and feedback that enables the prompt reporting of errors and establishes a real-time feedback loop between testing experts and developers.
  4. eliminates mistakes made by humans or by hand since Selenium automation testing can carry out exact tasks even in challenging testing conditions.


AI-assisted Automation testing with selenium solutions is one of the newest developments in test coverage that can help take test coverage to the next level. These tools can gradually increase their test coverage by learning more about the target application through self-improving machine learning.


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