It works better when the software functions without errors and only when a reliable QA strategy exists. In today’s digitally driven corporate ecosystem, enterprises prefer a software testing method that saves time, runs various test scenarios, and employs the latest technological advancements to pinpoint the bugs.
While test automation does the job, the combination of Artificial Intelligence (AI) and Machine Learning (ML) with test automation takes the QA process to the next level. In tandem, Machine Learning in test automation delivers better and effective results without the need for manual labor, i.e., you will not have to re-write and refine your test cases for different scenarios.
But ML does more than this when infused with test automation. Read on to know what they are.
Machine Learning for Test Automation
Let us start with test automation. It is the use of testing tools (such as Selenium) to write test scripts with required input values and execute them to receive the results. The only manual work you would have to perform is to define the test case in the form of a programming script; the tool takes care of the rest of the testing process.
Sounds easy, right?
However, test automation has a drawback despite its benefits: it requires constant and consistent monitoring as testing software receives updates. To tackle this issue, ML takes charge; it helps to automatically generate and update test cases, find errors, and improve the existing code scope. Thus, it assists enterprises in creating higher quality and quantity of work in less time.
Furthermore, machine Learning in test automation ensures that you can put every change with every possible input through QA. Doing so by manual testing or even test automation would take considerable time and cost.
6 Things to Consider When Applying ML/AI in Test Automation
You now know how Machine Learning and test automation together create an unbreakable and dependable software testing strategy. But before you jump in to adopt it in your company, here are six things that you must consider before and when applying machine Learning in test automation:
• Automated User Interface (UI) Testing:
Website visuals are usually fascinating to test manually, but the human eye can still miss some broken elements on the page. ML works best here; it employs image recognition technology to identify and verify UI bugs.
• Working on Unit Tests:
Using ML to create and run unit tests saves developers considerable time to focus on writing code for the software. Authoring and maintaining AI-based unit test scripts also helps in the later stages of the product life cycle.
• API Testing:
When an API test enters the scene, the comfort and ease often walk out. Even without ML/AI, API testing is quite daunting, as it requires you to understand the functioning of the API and formulate test cases and scenarios.
With machine Learning in test automation, you can record API activities and traffic to analyze and create tests. But to modify and update 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 alter the test scripts; that means you will have several test scripts you need to qualify as useful. AI and ML-based tools predict whether the test application requires multiple tests. It helps you avoid executing unproductive test cases and save time and money.
• AI and ML-based Test Data Generation:
AI models work on datasets. Similarly, test scripts require input data to run. You can employ machine Learning in test automation to generate data sets resembling personal profile photographs and information like age and weight.
The information is based on trained ML models using existing production datasets to learn. The datasets created in this manner resemble the production data ideal for software testing.
• Robotic Process Automation (RPA) for Regression Testing:
RPA helps automate existing IT systems and maintains them simultaneously. It scans the screen, navigates the systems and functionalities, and identifies and collects data. Powered solely by robots, all the tasks are automated and run using the web or phone applications.
Additionally, its prime advantage is scalability, cost-saving, improved productivity, codeless testing, and accurate outputs.
Future Applications and Opportunities for ML and Test Automation
In test automation, we have only scratched the surface when it comes to using AI and ML. Both these technologies are still developing and have tremendous potential that can add considerably to the current test automation scenario.
For the future, machine Learning in test automation has a lot to offer to IT enterprises, and here are some of those applications and opportunities you can expect to come up:
ML will help make test automation the go-to strategy, ultimately skipping over manual testing. While the latter will exist, companies will adopt a culture that prefers test automation for frequent testing.
Quality and accurate results would become commonplace among IT organizations as AI and ML take the lead to generate, train, run, and provide outputs in less time 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 intelligent tools will likely make life easier for testers and coders, be it UI or API testing.
The rise of Predictive Test Selection is highly likely to assist organizations struggling with running huge datasets. Testing even a small change often takes hours and days for many IT enterprises to produce feedback. Predictive Test Selection processes incoming modifications and runs tests that are most likely to fail.
At last, now and in the future, combining test automation and ML will save time and cost, prompting companies to use this duo widely across every department in the company.
In our opinion and knowledge, machine Learning in test automation is the right step in the right direction that will equip businesses across industry verticals to augment their testing process.
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 coding knowledge. On the other hand, RPA offers a drag and drop feature to automate tasks that doesn’t require programming knowledge
Test automation is usually implemented across multiple environments like QA, Production, Performance, and UAT, while RPA only requires a single production environment
2. How can you leverage natural language processing for creating tests?
Testers can seamlessly create test cases based on the requirements of a customer obtained from user stories. For a tool using NLP to do so, a tester is required to input the following details for the software to generate accurate test cases:
User story— This contains the requirements and descriptions of software features offered by the end-user
Acceptance criteria— it is a description of how the created software must function to meet the provided requirements
Test scenario description— this shows the interaction between the user and the product that will generate the test case
Dictionary— It consists of keywords that the program utilizes for generating test cases
3. What is the key difference between supervised and unsupervised machine learning?
Supervised learning primarily refers to the technique that requires labeled data to train the model, while unsupervised learning doesn’t require any labeled dataset
Supervised machine learning primarily is leveraged for classifying data or making predictions, whereas in the case of unsupervised machine learning is used for understanding relationships within datasets
Unsupervised machine learning can be more complex to reach adequate levels of explainability due to a lack of human oversight
Supervised ML is more resource-intensive due to the requirement for labeled data
4. Why is test automation often unstable without the utilization of ML technologies?
Some of the root causes for this instability are:
The testing stability of mobile and web apps is affected by elements within them, which are either dynamic by definition or the ones modified by the developers
Modifications made to the data on which the test is dependent on impact the testing stability. Changes are often made directly to the app, like new screens, buttons, user flows, or user inputs
As the non-ML test scripts are static, they aren’t able to automatically adapt and overcome the changes, and this inability to adapt leads to test failures, flaky tests, build failures, inconsistent test data, and more
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