It works better when the software works correctly and only when a reliable QA strategy is in place. In today’s digitally driven enterprise ecosystem, companies prefer a software testing method that saves time, performs various testing scenarios and uses the latest technological advances to pinpoint the bugs.
While test automation does the work, combining artificial intelligence (AI) and machine learning (ML) with test automation takes the QA procedure to the next level. At the same time, machine learning for automation delivers better and more effective results without manual work You don’t have to rewrite and refine your test cases for different scenarios.
But ML does more than that when infused with test automation. Read on to learn what they are.
Machine Learning for Automation Test
Let’s start with test automation. It uses testing tools (like Selenium) to write test scripts with required input values and run them to get the results. The only physical work you would have to do is to define the test case in the method of a programming script; the tool takes care of the rest of the testing procedure.
Automation Requires Constant & Consistent Monitoring
However, despite its benefits, test automation has a downside: it requires constant and consistent monitoring while the test software receives updates. To address this issue, ML takes responsibility; It helps to automatically generate and update test cases, find bugs and improve existing code scope. In this way, it supports companies to create a higher quality and quantity of work in less time.
In addition, machine learning for automation ensures that you can make any change with any possible input from QA. Achieving this through manual testing or even test automation would take a lot of time and cost.
6 things to consider when applying Machine Learning for Test Automation
You now distinguish how machine learning for automation test work together to create a resilient and reliable software testing strategy. But before you implement it in your organization, here are six things you need to consider before and while applying machine learning in test automation:
• Testing the automated user interface (UI):
Website visuals are typically captivating to test manually, but the human eye can still miss some erroneous elements on the page. ML works best here; It uses image acknowledgement technology to identify and verify UI bugs.
• Working on unit tests:
Using ML to create and run unit tests saves developers a lot of time focusing on writing code for the software. Creating and maintaining AI-based unit test scripts also helps in the later stages of the product lifecycle.
• API Tests:
When an API test arrives the scene, the convenience and ease often go away. Even without ML/AI, API testing is pretty daunting as you need to understand how the API works and formulate test cases and situations.
With machine learning for automation test, you can highest API activity and traffic to analyze and create tests. But in order to adapt and apprise the tests, you would need to understand the nuances of Representational State Transfer (REST) calls and their parameters.
• Several test scripts:
Any updates, upgrades, or code changes require you to modify the test scripts; This means that you must have several test scripts that you must qualify as useful. AI and ML-based tools predict whether the test application will require multiple tests. It helps you avoid running unproductive test cases and save time and money.
• AI and ML-based test data generation:
AI models work with data sets. Similarly, test scripts require input data in order to run. You can use machine learning in test automation to generate datasets that resemble personal profile photos and information like age and weight.
The information is based on trained ML replicas that use existing production datasets for learning. The data sets created in this way resemble the ideal production data for software tests.
• Robotic Process Automation (RPA) for regression testing:
RPA helps to automate and maintain existing IT systems at the same time. It scans the screen, navigates through the systems and functions, and identifies and collects data. All tasks are powered solely by robots and performed through the web or phone applications.
In addition, its main benefits are scalability, cost savings, improved output, codeless testing, and accurate outputs.
Future applications and opportunities for ML for Automation Test
In test automation, we’ve only scratched the surface when it comes to using AI and ML. Both technologies are still under development and have enormous potential that can significantly complement the current test automation scenario.
Going forward, machine learning in test automation has a lot to offer IT organizations, and here are some of the uses and opportunities you can expect:
- ML will help make test automation the go-to strategy and ultimately skip manual testing. While the latter will persist, companies will adopt a culture that favors test automation for frequent testing.
- Quality and accurate results would become commonplace in IT organizations as AI and ML take the lead to generate, train, execute and deliver results in less time and with minimal cost.
- The problem of too many test cases or too few test cases will find a solution in AI-based test generation tools. These smart tools are likely to make life easier for testers and programmers, be it UI or API testing.
- The rise of predictive test selection will most likely help organizations struggling to manage huge datasets. Testing even a small change often takes hours and days for many IT organizations to get feedback. Predictive Test Selection processes incoming changes and runs tests that are most likely to fail.
- Finally, the combination of test automation and ML will save time and money now and in the future, prompting companies to use this duo extensively in all departments of the company.
Traditionally, there is a pyramid model for technology with artificial intelligence (AI) at the top. Below are the technological building blocks required as platforms to make the AI work. First, let’s look at how this pyramid is formed.
Starting from the bottom, the fundamental technology layer is digitization. Automation, machine learning, and ultimately AI are not possible without digitization. Digitization is the process of converting non-digital, often analog, data into digital storage. A spreadsheet is an example of digitized data, as are scanned images.
At the instrumentation level, data and technology begin to interact, and this is where companies can sit up and take notice. At the instrumentation level, technology is used to observe or measure the digitized data as the information is exchanged between systems or people. However, the method only works with the data that is already available and does not bring any new insights. In the instrumentation phase, a simple level of automation is often already in place: simple heuristic rules are applied to route the data.
When data science and mathematics begin to manipulate digitized and instrumented data, the level of analytics is reached. Analytics makes it possible to extract meaningful insights from big data, allowing the data to guide companies in a dynamic process of decision-making.
Machine learning begins when programs begin to take analysis and apply it without explicit programming—the results of machine learning are somewhat independent of their programming. At this level, machines take in data and analyze it on their own, improving results in ways beyond what an analytical model can provide. Machine learning means that algorithms automatically improve with experience – essentially, the machines learn as they go. This is an essential part of any artificial intelligence model and has numerous applications in business and industry.
The holy grail of sci-fi technology. AI emulates human thinking. Part of the model for AI requires machine learning. In the broadest sense, however, AI goes beyond machine learning by generating human capabilities such as visual processing and language understanding.
AI and Automation
AI and automation cannot be confused with the same thing – where there is automation; artificial intelligence does not have to be involved. In fact, automation has been around for centuries, much longer than we had computers: traditional milling used waterwheels to automate manual processes that would otherwise have required human labor. Water turns the wheel that turns the millstone – an automated process that is decidedly unintelligent. Simple automation has been the cornerstone of many companies for years. For example, a process for sending invoices can be automated once entries in spreadsheets have been confirmed by people in accounting.
Automation means that machines emulate human tasks. But the, AI requires that the machines also emulate human thinking.
Frequently Asked Questions
1. What is the difference between RPA and test automation?
Some of the key differences between test automation and RPA are:
- Test automation automates repetitive test cases, while RPA automates repetitive business processes
- Test automation requires programming or programming knowledge. On the other hand, RPA offers a drag-and-drop capability to automate tasks that don’t require any programming skills
- Test automation is typically implemented in multiple environments such as QA, production, performance, and UAT, while RPA requires only a single production environment
2. How can you use natural language processing to create tests?
Testers can seamlessly create test cases based on a customer’s requirements gleaned from user stories. A tool that uses NLP to do this requires a tester to enter the following details in order for the software to generate accurate test cases:
- User Story – Contains the requirements and descriptions of the software features offered by the end user
- Acceptance Criteria – it is a description of how the software produced must function in order to meet the requirements provided
- Test scenario description – shows the interaction between the user and the product that generates the test case
- Dictionary — It contains of keywords that the program uses to generate test cases
3. What is the main difference between supervised and unsupervised machine learning?
- Managed wisdom primarily refers to the technique that requires labeled data to train the model. At the same time, unsupervised learning does not require labeled datasets.
- Supervised machine learning is primarily used to classify data or make predictions. At the same time, unsupervised machine learning is used to understand relationships within data sets.
- Unsupervised machine learning can be more complex due to a lack of human oversight to achieve a reasonable level of explanation ability
- Supervised ML is more resource intensive as machine learning with labels has a large impact on mobile app development
4. Why is test automation often unstable without using ML technologies?
Some of the main causes of this instability are:
- The test stability of mobile and web apps is affected by elements within them that are either dynamic by definition.
- Changes to the data on which the test depends affect test stability.
- Because the non-ML test scripts are static, they cannot automatically adapt and overcome the changes. This inability to adapt principals to test failures, broken tests, build failures, inconsistent test data, and more.
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