Role of AI to improve Test Coverage
Introduction to Artificial Intelligence
As the world keep on progressing, people as well as organisations’ needs and requirements keep on changing. As a result, new technologies keep on developing to meet the needs and requirements of customers and organizations. Artificial Intelligence has become one of the most important and widely used technologies over the past few years which has immensely helped people from all streams. Software Testing is one of the domain where Artificial Intelligence has immensely helped software testers. AI has ensured smoother testing of complex software applications thus making the testing process more efficient. AI ensures faster generation of test scripts thus resulting in better test coverage and productivity. Whatever bug tracking or test scenarios are missed through traditional testing,AI covers those test scenarios and bug tracking. This blog will talk about the role of AI in test coverage.
Test Coverage
Test Coverage is a measure of amount of programming codes covered for a software application by test cases. Test coverage measures the programming code of software application at different levels like Function Coverage, Statement Coverage, Branch Coverage and Path Coverage etc. Higher test coverage ensures that more programming codes of the application is tested thus making sure that more bugs are identified before software release . As a result a good quality and robust software application is delivered to customers. It is a type of black box test technique where testers check the functionality of applications without looking at code. Test coverage also measures the number of test scenarios and its corresponding test cases covered for every requirements of software application and the requirements are covered in different documents like functional requirement specification document, software requirement specification document and customer requirement specification document.
OverTesting
OverTesting or test redundancy refer to testing the same test scenarios as well as running the same test script again and again in different ways.If the same test scenario is run multiple times it becomes redundant resulting in poor test coverage and non identification of bugs. The various disadvantages of overtesting are:
Time Wastage is High: The repeated testing of same test scenarios does not yield any fruitful results. It is just liked knocking at the door of a house when we know nobody is present in the house. So testing same test scenarios again and again result in wastage of time which could have been spent in testing other basic and critical functionalities of software application.
Wastage of Resources: Testing the same test cases again and again result in high wastage of resources like more usage of test tools and increased cost of testing. As a result more functionalities remain untested and many critical bugs remain unidentified which can severely affect the performance of software applications.
So it becomes very important for testers to not perform overtesting but instead utlise the time in finding out the critical bugs present in each and every functionality so as to ensure that a quality software application is delivered to customers at short period of time.
How AI helps in better Test Coverage
- Increased Test Scope : AI helps to detect those functionalities of software applications that were not detected through traditional testing process thus ensuring rigorous testing of the application. It also helps testers to find out the potential defects thus increasing the safety of software application.
- Automated Test Script Generation: AI interacts with the software applications and generate more amount of test scripts in short span of time. As a result greater number of test scenarios are covered , testing process becomes smoother and test accuracy is increased as compared to testing without use of AI.
- Efficient updates of Test cases: Non updation of test cases result in non detection of critical bugs which can hamper the application and this phenomenon is known as “pesticide paradox”. So it becomes necessary to update the test cases frequently due to constant updates and code changes in software application which may become a tedious task through traditional testing process. With the use of AI updation of test cases becomes faster thus making the entire testing process more efficient.
- Effective in prioritizing test cases: AI helps testers to prioritize test cases by finding out all the critical functionalities present in the software application. This also reduces the testers efforts most of which could have been spent in writing test cases for less priority functionalities but instead efforts will be spent in identifying the critical features of application .
- Better code analysis: AI tools helps in improving the code analysis by tracking those parts of programming codes which are not covered through traditional testing process. This results in maximum code coverage and help developers in writing performing unit testing of the untested functionality of software applications.
- Improved Visual Testing: Visual testing refers to testing the proper display of visible elements of software applications like color, images and fonts across different browsers and operating systems. AI tools ensure better visual testing and help to give a clear distinction between actual and perceived display of user interfaces of software applications. As a result testers get a better view about how all the visible elements of software applications appear in different browsers and operating systems.
AI Tools
Some of the most commonly used AI tools are mentioned below
Example of Creating Test cases for amazon login page using AI
Below is the screenshot of the login functionality of amazon login page for which we have to create test cases using AI tool.
Here ChatGPT is used for creating test cases of Amazon username text field. ChatGPT is one of the AI tools frequently used by Software Developers and Testers for performing their tasks.
Below is the ChatGPT home page. In the ChatGPT text field we are giving the task for chatgpt to perform a task which is to create test cases of email text field for amazon e commerce application The task to be performed is mentioned under “Prompt” and the element on which the task is to be performed is mentioned under “Requirement”. By clicking on the arrow present on right hand side or pressing “ENTER” button, chatgpt will generate all possible test cases for email text field within a minute which would have taken about one hour to complete through human efforts.
Below are the screenshots of all the possible test cases of the email text field of Amazon e-commerce application generated by ChatGPT
Conclusion
Artificial intelligence and its tools have been playing a big role in improved test coverage. AI tools covers those functionalities of software applications which were left uncovered through traditional testing processes.It reduces test redundancy thus ensuring more test scenarios are covered in short span of time. It generates test scripts automatically in short period of time thus ensuring effective testing of software application.AI tools help testers to adapt to the constant updates taking place in the software application. However for academic learning perspective it is not recommended to use AI tools for test cases and test script generation.
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