A New Way to Learn AI Machine Learning and AI Simple Testing API

Posted By : Sanjana Singh | 30-May-2021

                                                          
                                                                              Image downloaded from: https://www.testim.io/wp-content/uploads/2019/11/Testim-The-9-API-Testing-Tools-You-Cant-Live-Without-in-2019-B.png

 

Machine Learning (ML) tools and Artificial Intelligence (AI) are the hottest buzzwords in Software Industry today. With the software testing market surge, the Testing community, Service organisations, and product/tool testing companies are trying hands on different tools.  

Software testing is as essential as Software development. 

 

The primary focus of Artificial Intelligence in software testing tools is to make the software development lifecycle easier. AI is used to automate and reduce mundane and monotonous tasks in the development and testing process through machine learning.  

While exciting work might be occurring in the Software Testing space, there seems to be a lot more. Unfortunately, it is not easy to find work, research or critical solutions from the surrounding fluff. 

 

Machine Learning has already proved to be an essential tool in Software testing. 

Imagine you need to change a small portion of the code that you wrote for an application. What would be the minimum number of tests you would perform to determine if the change helps the code or not? 

Well, AI tools come to the rescue here. Using ML, you can precisely predict the number of tests required. 

 

 

Use of Codeless Functional Test Automation

One of the most popular themes right now is "Codeless Functional Test Automation", - where we allow machines to see how you can use software for the product under test. Quick research around "Codeless test automation" or "Use of AI in test automation" will show several tools available in this space.

 

 

How does a read-enabled API-test-creation task work?


So, a test starts by testing your API traffic and releasing logical API test conditions. To facilitate the robust data exchange needed to create these test scenarios, AI under the SOA test takes steps to capture and reassemble data for API calls, connect them, extract the appropriate response data, and make automatic retrieval tools.

 

 

Rules to be followed- 

These rules aren't just basic testing steps - they show relationships, such as, if I put something in my cart with an API phone, the cart ID will be vital after I call the next to check the items in the cart. SOA test understands this relationship automatically; however, humans are more intelligent than robots (Sorry Robocop fans), so they can extend the rules by adding more context to each API resource.

So to extend our earlier example, while it is essential to get a cart ID, I might want to confirm the number of items I've added to my cart.

 

If the SOA test has not yet taken the ID, one can log in and add a new rule. Once we have defined the connection to the cart item as stated, every time we use an API in the future, the SOA test will automatically complete any tests which affected that particular API with the relevant details to deal with that requirement. 

 

AI helps you perform repetitive actions by automatically learning how to test your API and how it should work. This means that the machine will remember all the necessary steps to be performed on specific APIs by simply using them. As a result, the cost of testing the APIs may decrease over time.

 

 

                                                        
                                                                       

Image downloaded: https://blog.qasource.com/hubfs/image/api-testing/api-testing.jpg

 

 

AI and Machine Learning are used to create the Automatic Test and Repair API 

Another good example, which adds machine learning to the mix, is the Parasoft SOA test's Smart API Test Generator, which surpasses recording and playtests, using AI and machine learning to convert manual UI tests into default API test conditions.

Smart API Test Generator uses thinking to understand patterns and relations in different API calls made while using the UI. This ability creates a series of API calls from that analysis representing interface calls made during UI flow. After that, apply machine learning by looking at the various API resources and saving them as templates in the associated data structure. We then update his internal structure by examining other test cases in the user library to learn the different behaviours when using APIs, such as vaccinations, or to place a specific title in the appropriate field.

 

The goal of Artificial Intelligence here is to create advanced tests (not to repeat what the user is doing, as you get with simple test recording and play). The tool detects patterns within traffic, creates a complete data model for visual parameters, generates automatic API testing. It also enables learned patterns in other API tests to improve them and help users make more advanced test scenarios. The resulting automated API testing is entirely usable, fabulous, and tolerant of change.

 


 

How to Apply Artificial Intelligence Applications in real world 

 

Challenges and Potential Problems

Problem: A large amount of data collection consumes a lot of time and is difficult to maintain.

Solution:

  1. Modify the similarity of the data within the AI system to segment and store it correctly.
  2. Update the machine ML algorithm to be able to identify and integrate all data collected with predictive statistics.
  3. Consider your computer options for ample data storage.

 

Problem: Data can be collected during events or unexpected situations, making it difficult to collect and use for training.

Solution: Update your programmed algorithm within the AI machine to rearrange the data collected during these unexpected events. So the AI system could see this data set as inconsistent behaviour during training.

 

Problem: People's bias may arise from training and evaluating data sets.

Solution: 

  1. Use technical tools and practice various problems designed to analyse the robustness of data sets. 
  2. Set up your AI machine during training to provide data testing facilities. 
  3. Manual manipulation of data analysis can ensure not to play any human preferences before proceeding with the process.

 

Problem: Errors increases and become more complex in AI systems.

Solution: 

  1. Map in detail all the expected behaviours of the AI machine and adjust test cases to match new insights. 
  2. Strengthen retrospective testing practices within the development cycle.

 

Use of Mechanical Learning Personalization Cell in Selection Testing

 Authentication and Verification of the UI layer before production is another vital part of your testing strategy. We widely accept The Selenium framework in UI testing as a clear user choice. However, users are still struggling with the everyday challenges of Selenium testing for storage and stability. This is where AI technology and, in particular, Machine learning can help to provide livelihood during operation to address the typical maintenance problems associated with performing UI tests.

 

We offer this to Parasoft Selenic, which can "read" the internal structure of your data while performing regular Selenium tests. The Selenic engine monitors run and take detailed information about the app's web UI content under test. It removes DOM objects, their attributes, location finders, etc., and links them to actions performed by UI-driven tests. Selenic uses Parasoft's data marketing method, which stores that information within its AI engine, and the model is continuously updated, analysing the historical performance of all tests to remain "efficient."

 

It is a critical time-saver in cases where UI objects for web pages are moved or modified in excess, causing tests to fail. With Selenic, AI heuristics used by the engine can "match" those modified objects with the historical data represented by the model. It automatically generates "smart locators" that resist changes by reversing Selenium test performance during launch. According to those changes, the model automatically disseminates details about these changes and updates the next generation of new locations. 

 

Performance of AI

Rules are not just rules but relations in a connected series. If the AI software does not choose to communicate, one can intervene and add a new rule. When the connection is defined each time, the AI software can test itself on a specific API with specific details such as specific requirements.

 

 It makes no sense after a repetitive act when you can train a machine to do that. But API testers need to do repetitive tasks and spend a lot of time understanding the functionality of the APIs and developing appropriate tests.

 

Now you may ask, won’t machine training take time? 

The answer would be NO. 

 

 First, there is the built-in testing tool for API that detects patterns. Secondly, there is no need for manual training. You can take a test case with a single click, translate the law, and install it inside the software. This could be done at any test level, be it a single test case or an entire test case.

Therefore, software built with AI can learn from the work done to improve the tests and use that knowledge automatically to create experiments that work in the same way when working in future APIs.

Such AI test tools use dynamic value parameter such as time and data detected in previous cases. But if the question is about validating something, then a new, smarter, and the more automatic view will work. So with the AI-enabled tool, there is a wide range of verification strategy by looking at other non-essential features.

 

With a lot of functionality built into the UI, a simple action can create many API calls that are over-tested or unnecessary. With some diagnostic tool software, calls are made and extracted to specific domains, helping you identify contextualised environment organisations be easy to use and simplify necessary testing procedures. With the addition of both technologies, there will be improvements in the API test area, and new enthusiasm for building more API test suits will follow. 

 

The value of adding machine learning in automatic API testing

API testers spend quite some time understanding how API work to build that functionality in their test environments. That much information and knowledge will never leave that one tester. To make it even more productive and efficient, you can automatically download all that information with SOA test's Smart API Test Generator and then expand it further to grow with your software. 

 

 

What else can we gain from the new SOA test machine? 

 

What else can you gain from the learning capabilities of the new SOA test machine? 

 

  • Training: In place of spending a lot of time to put together new API testers, training them on how APIs work, and create meaningful test environments, SOA test can come in handy. So SOA test already understands the relationship between APIs. New API testers can start popular testing APIs quickly and add value instantly, rather than wasting time learning how to put it all together. 

 

  • Capture Info: Using AIAtest-enabled and more trained API testing tool, users can define a single object, lock it with an intelligent test template, and share that template with a large body of testers, especially important for agile development given set contact times testing specific APIs across the group.

 

  • Consistency: You have to rethink what you want to accomplish to ensure the need. Every time you build an API test, make sure all of those connections, your authentication and verification details set for each API, everyone agrees on how you should test them. You will have a consistent API test built against each display in the same way at all times. This is also good for correcting errors because when all APIs follow a specific pattern, it becomes easier to understand where things can go wrong. After all, there are general rules.

 

  • Reading: Instead of trying to manage and understand the complete API list, users can benefit from the SOAtest intelligent resource library, which helps users to quickly see which APIs have been tested and add additional rules for untested APIs so that when revealed as part of a situation, a successful test we are as reasonable as possible.

 

Conclusion

 

Agile development helps organizations deliver quality software to market it faster. Without the necessary technology to help organisations fully evaluate their applications more quickly, the risks associated with faster delivery eliminate existing Agile benefits. Organisations should be innovative about API testing.

 

A robust API testing strategy will allow organisations to get the most out of their Agile conversions. Test tools must work for us to make this a reality. Using Artificial Intelligence to make complex suggestions for creating API test scenarios reduces the complexity associated with API testing, reduces its acquisition barriers, and help organisations come up with a manageable, sustainable, and scalable testing strategy.

 

Over the past few years, our strategy is indulging in embracing future technologies to simplify critical testing methods. I hope, with these new additions to our technology, you will be able to maximize your coverage of the API test with the renewed joy of building experimental API testing suits.

 

 

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