How to improve code coverage of Regression Testing without writing scripts

 The stability and dependability of software applications are crucially dependent on regression testing. Retesting changed or recently added features are necessary to find any problems or unforeseen side effects. Code coverage is one crucial indicator in regression testing which gauges the degree to which the source code has been tested. Selenium automation training online helps to reduce errors, and the overall quality of the software is improved by increasing code coverage. Although creating scripts was typically required to increase code coverage, other methods can do the same without using hands. This article investigates practical strategies for improving code coverage in regression testing without scripting.

Prioritizing the test cases:

Test case prioritization is one method for increasing code coverage without scripting. You can concentrate on crucial components of codebase by prioritizing test cases according to various criteria, such as risk, functionality, or code complexity. Determine high-risk features or details first because they are more likely to introduce errors. Prioritizing test cases for these domains will provide thorough coverage of essential functionalities, leading to enhanced code coverage.

Indicator value analysis:

Boundary value analysis is an additional strategy for improving code coverage. This approach is designed to test the limits or extreme values of the input variables. You can improve your chances of finding flaws relating to boundary conditions by pushing values at the upper and lower boundaries and those slightly outside those limits. For instance, if a function requires a parameter to fall within a specific range, test it with values just on the edge of that range or outside of it. Selenium free certification course teaches you how to improve code coverage results by using this technique, which identifies problems that may go undetected by random testing.

Comparability partitioning:

Equivalence partitioning is a method that creates a set of equivalent classes from the input domain, each style representative of specific behavior. One representative value from each equivalence class needs to be tested, not all potential values within a range. With this method, you may handle a variety of circumstances with a small number of test cases. You can increase code coverage without using a lot of scripting by carefully choosing the representative values.

Pair testing and code reviews:

Pair testing and code reviews are two effective ways to increase code coverage. During code reviews, developers and testers work together to examine the code and find any potential problems, such as missing test cases or insufficient coverage. Regression testing efforts can be made more effective by utilizing the knowledge of various team members. Through sharing ideas and contrasting points of view during test execution, pair testing, in which two testers collaborate on test execution, can also find blind spots and increase code coverage.

Data generation for tests:

More excellent code coverage can be achieved by producing extensive and varied test data. You can develop various test scenarios using strategies like random data generation, combinatorial, or model-based testing. These strategies increase the likelihood of covering previously untested code parts by assisting in exploring various routes across codebase. You can improve code coverage without scripting by often changing the input data.

Investigative testing:

Exploratory testing is an informal strategy emphasizing discovering, researching, and experimenting with the tested application. It encourages testers to apply their domain expertise and intuition to find bugs as they are being developed. Testers can discover unexpected behaviors and edge cases that may have yet to be considered during scripted testing by browsing the program without following a predetermined test script.


Summing it up:

From the above mentioned, conducting a thorough code coverage study is essential to get a more accurate evaluation of the caliber of your program. Using a code coverage tool can ensure high-quality future product releases for existing software that lacks automatic tests. There are various ways to get high test coverage, but assessing the methods used to achieve them is crucial to ensure they continue to be effective.


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