Artificial intelligence (AI) is making inroads in quality assurance. The benefits of AI in test automation are many. Such include better quality testing and higher efficiency.
Machine learning helps identify patterns for the prediction of future trends.
AI in test automation; what is its role?
Automation helps quality assurance (QA) specialists increase efficiency in processes. Advantages of using such technologies include:-
The ability to handle repetitive tasks:
- Generation of relevant data for decision making
- Early detection and correction of bugs, amongst others.
There is a need to continuously improve the functionality of the tools. AI can help with configuration and increase the reliability of the results. It is an excellent transition that reduces dependence on manual testing.
Unit tests creation and updates
Unit tests play a role in continuous testing, integration, and delivery. All these processes are a part of DevOps. The tasks can take a lot of time and can be quite challenging for developers.
The teams can cut down on time with the use of AI products. Developers can create and update test scenarios in a very short time.
It is very important to note that there are some limitations. The capability of AI-generated unit tests depends on and mirrors the codes you build them on. It limits their ability to guess the final functionality of the code, outside of your specifications.
If there are any deviations in behavior, the AI unit test will not detect them. The developers need to come up with complex business logic that can take care of such scenarios.
Image-based testing
Manual visual testing continues to have an active role in quality assurance. It can, for example, help verify how the user interface (UI) looks to end consumers. It takes into account things like color, size, and shape.
Developers can also check that there are no overlaps with the different UI elements. In this case, it has nothing to do with the functionality of the application. Such a process can work well with the use of AI.
Machine learning (ML) validation tools can detect errors humans may miss. It is achievable with ML tests that can detect visual bugs in software. AI can also cover larger test areas.
It looks at operating systems, hardware requirements, and browsers. It then determines applicable UI standards while considering the end user’s needs.
Increasing confidence levels with machine learning (ML)
Establishing confidence levels is the process of estimating the performance of ML algorithms on unseen data. Let’s try and simplify the definition a little with an example.
You may develop an algorithm to identify images of people. Now take 50 images of people, tag them as such, and feed them into the algorithm. With ML, the system will learn to identify people with the images you have provided.
What happens if you provide an image of a person you did not have in the initial 50? Well, the ML will still identify it as a person. It bases its decision on the data you provided in the initial stage. It looks at the features like the head, shape, legs, arms, etc.
It then compares these attributes to the other 50 images you had provided. It then comes to a high confidence level conclusion that it is a person.
So how does this apply to test automation?
Developers can test different elements of the software. They use test scripts that capture different attributes and values. They must feed the relevant information into the algorithm so that ML takes over.
They can then test the actual application. Even if there is a deviation (in this case, the new image), it can still use the data to continue with the tests.
API test generation automation with AI
API evaluations measure the quality of interactions between programs that communicate with protocols, servers, and databases. It results in more stable connections and correct processing of requests.
The use of AI in automation allows for the generation of many test cases for the API QA process. It also helps with the evaluation of the functionalities of any third-party tools. Automation is especially critical if you are using hundreds of APIs.
Testers can get a lot of information. Such include the relationship that exists between the different APIs. They can then use this data to understand or update existing tests.
It also provides an avenue that helps with the creation of new tests. Testers can base them on a lot of different scenarios.
Test maintenance
A test cycle can break if there are any changes to the code. It is not uncommon to have to rerun an entire test suite. It can be due to application changes that you cannot even trace. It is crucial to keep up with evaluations to identify such changes.
The teams must then take the appropriate steps to fix such codes. Continuous integration and testing yield a lot of data. But, it would be time-consuming to go over all of it to identify common patterns. AI can take over such tasks for testers and developers.
AI tools will monitor existing tests. In the case of a failed test, AI will choose the best UI elements to try out with modified codes. ML will guide the number of test cases the teams have to run with the modified code. It is critical to do so before using it on the final application.
Further, AI can identify and supplement gaps that could impact the success of the tests.
Geographic Information System (GIS) technology software testing
Geographic Information System (GIS) technology has many uses. It can help provide estimations for damages arising from natural catastrophes. It also helps improve areas like Logistics, energy, and public health.
However, there is not much in terms of GIS testing. The approach leans towards validation through integration, classic unit, and system-level tests. Yet, GIS applications are becoming more useful every single day. Mobile apps, for instance, use GIS and localization functions.
Developers must integrate testing in the processes to ensure proper security, functions, and an excellent user experience.
There are some distinct areas QA Specialists need to work around.
- GIS software development requires the testing of large amounts of diverse data. Such include attribute data and spatial coordinates. Finding the right way to handle such data is critical.
- The teams must choose between using pre-modified or real data, depending on the stage of the quality assurance process
- There is a need to develop specific GIS software testing methods for each unique geospatial application. It is not possible to use standardized testing due to the diversity of GIS software. Yet, the challenge remains that there are no specific technological criteria for GIS testing.
Manual handling of the data would be a challenge to the QA teams. There is a need for more efficient processes, which is achievable with automation.
AI can help with complex simulations and the analysis of intricate applications. Thorough data, filter, and performance tests will ensure quality products reach the market.
Final thoughts
Test automation is critical in software development. It helps identify errors early on in the cycle.
The teams can then make the necessary corrections. Automation helps improve efficiency that is not achievable with manual testing. The processes are faster, and the results are more reliable.
And now, the use of artificial intelligence will take it a step further. Developers can execute tests over more expansive areas. ML learns patterns and uses the data to improve the reliability of the tests.
AI and test automation are, indeed, a space worth watching. There is a lot of potential for innovation and growth.
This Article Source is From : https://www.geospatialworld.net/blogs/ai-in-test-automation-heres-how-it-works/
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We are in the crawling stage when it comes to applying machine learning.”- - Dave WatersTechnologies and their interdependencies are paving the way to a future that is smarter and intelligent to fulfill the demand for resources.
Cloud providers like AWS, Google and Microsoft offer Cloud with ML capabilities that require scads of processing power.
Undermentioned are a few applications of Machine Learning Algorithms using the Cloud Cognitive Cloud - There are millions of people using cloud and they produce massive amounts of data frequently.
The pre-existing unstructured datasets act as a source of information for the machines to learn from and adversely the cloud provides applications with sensory capabilities that are used to perform cognitive functions and make prospective decisions.
Chatbots providing solutions through chat has brought them into focus in the customer service industry eliminating human interaction, hence reducing any errors.
IoT Cloud - Gartner predicts that by 2022, more than 80 percent of enterprise IoT projects will include an AI component, up from only 10 percent today.
When hearing ‘AI for Customer Service’ most would immediately think this is going to be ‘yet another article about chatbots’. No wonder. Chatbots have been one of the hottest topics recently. However, our experience shows that AI can be applied to many other areas of Customer Service business as well. And those possess even more potential to boost your business.
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