With the software development life-cycle becoming more complex, and the breakneck pace of new product launches, there is no other choice than to make the software testing process smarter, faster and better. New age technologies such as RPA, AI and ML are getting increasingly adopted to accelerate the software development process. The use of Artificial Intelligence in software development is still at a beginning stage. Through the application of reasoning, problem solving and in some cases Machine Learning, AI can be used to support automation, decrease the amount of mundane and tedious tasks in the development and testing phase.
The key value proposition of AI is the fact that it can reduce the direct involvement of the developer or tester in multiple routine tasks. For example, most of the test automation tools run all the test cases and then deliver the required results. Most of the frameworks don’t have any in-built feature to analyze and decide which tests to run, so they end up executing all the test cases or some of the scheduled sets. An AI-enabled solution could review the current status of test, recent code changes, code coverage, and other metrics, and decide which tests to run. Bringing in decision-making that’s based on analyzing the data is an example of applying AI which could replace the developer or tester in the decision-making process.
Use Cases – Integrating Artificial Intelligence in Testing
Generating Unit Tests: The simplest use case for applying AI in software testing is generating unit tests. AI can help with the static analysis of the software to decide which areas of the code are not covered by unit tests. The tool can use this information to create unit tests for uncovered code and even provide input for these unit tests. It’s easier for an AI assisted tool to create unit tests, as these types of tests don’t need any setup or interactions with additional components. In the future, AI will be able to update unit tests each and every time the source code changes, and hence AI will be capable of maintaining the unit tests. This will help the developers to spend lesser time on test maintenance.
However the AI-based system need to be skilled enough to know what sections of the code require stubbing or mocking in order to separate them. In order to excel at this skill, the system has to learn to define dependencies in order to stub them correctly. This training process needs a serious amount of time to explain the AI-powered process on how to build up unit tests. Once the AI has reached a point where it can build mocks and stubs by itself, it will help the developers save tremendous amount of time and effort while creating unit tests.
API Testing: Application Programming Interface test generation goes hand in hand with the user interface that lies on top of it. AI attempts to understand the patterns and relationships in different API calls made while exercising the UI. From that analysis, it can create a series of API calls and provide parameters to test. By analyzing the behavior of users, AI can produce more advanced patterns and inputs for testing the API. The generated tests cover more edge cases and make sure a higher level of quality for your API.
Continuous Testing: In the AI-powered continuous testing process you can detect the altered controls more efficiently than a human, and with constant updates to its algorithms, even a simple minute change can be observed. Risk-based automation helps the users in determining which tests they must run to get the greatest coverage with limited time to test any critical feature. By the combination of AI in test creation, execution and data analysis, testers can update the test cases manually frequently and identify the controls, spot links between the defects and components in an effective manner.
UI Testing: In automation testing, Artificial Intelligence can be used in the object application classification for all the user interfaces. The recognized controls are categorized when you create the tools and testers can pre-train controls that are usually seen in out-of-the-box setups. When the hierarchy of controls is identified the testers can create a technical map such that the AI is looking at the Graphical User Interface (GUI) to acquire labels for the different controls.
AI-based Test Automation Tools
- Testimo io: This tool uses Machine Language for the authoring, execution, and maintenance of automated tests. It provides functional, end-to-end testing and user interface testing.
- Appvance: Appvance makes use of Artificial Intelligence to generate test cases based on user behavior. The portfolio of tests broadly covers what actual end users do on production systems. Hence, this makes it 100% user-centric.
- Test.ai: Test.ai is a mobile test automation tool that uses AI to carry out regression testing. It is useful when it comes to analyzing the performance metrics on your application and is more of a checking tool than a functional testing tool.
- Functionize: This tool leverages Machine Learning for functional testing for faster test execution.
- Applitools Eye: An automated AI testing platform targeted at automation engineers, DevOps and front-end developers who need to make sure their mobile, web and native apps are working as expected.
- AutonomIQ: It is a platform that is used to automate the whole testing life cycle of an application from test case creation to impact analysis. It helps testing team to speed up their test cases, data and scripts formation.
- Mabl: This is a Machine Learning driven test automation for web applications that simplifies the creation of automated tests. It also helps to find regressions tests and automatically maintain the tests.
- Parasoft SOAtest: One of its latest releases, introduce AI to alter manual UI tests into automated, script less API tests.
Challenges in AI-Based Test Automation
The major challenge in the AI based testing is to select the type of testing. AI optimization can’t be applied to all the form of testing. Functional and unit tests are easy to create using AI. The testing types like integration tests are harder to create; hence they require a more advanced setup and complex flow. The other key challenges for AI-based testing include:
- Finding the exact use cases.
- Lack of awareness as to what actually needs to be done.
- Verifying the actions of the apps based on the data that has been input.
- Testing the app for functionality, performance, scalability, stability, & security.
Technologies like AI, RPA and ML are making their way into the software development lifecycle. From improving accuracy to overcoming the limitations of manual testing, integrating AI-based solutions can significantly improve the software testing process, making it faster and more efficient. Setting-up an AI system might have some initial investment, but on the long run it will surely help enterprises save effort and cost
Software Test Engineer, RapidValue