Are you struggling to create your automation test scripts? If we had to take a guess, we would assume that you are still using traditional code-based practices to write the test cases manually. So what’s the solution? We strongly recommend to start investing in AI testing so that you can add advanced features like natural language processing.
With the inclusion of this process, you can create the automation test scripts in simple English language. Then? The system will automatically convert it into a programming language that you choose!
Unfamiliar with NLP in automation testing? Don’t worry! We are here for you. Our article will help you understand how natural language processing is changing the quality assurance testing processes. We will also give all the strategies that you must keep in mind to use its power in automated test cases.
What Is NLP
As we already said earlier, it is a very important part for closing the gap between human communications and computer knowledge. Using this approach, you can add computational linguistics, machine learning, and deep learning models to understand text and speech information.
When you’re adding natural language processing with automation testing, you’re essentially fulfilling the following parameters:
You can translate plain English test scenarios into test scripts which the system can execute to verify the functioning of all the elements present on your application.
- You can also analyze and understand various user requirements that will be written in plain language.
- This process can also help you to find potential bugs within the application infrastructure. It uses sentiment analysis or anomaly detection that might be present within the application test logs.
- Finally, the entire implementation of natural language processing will have a very important role in improving the communication between the development, quality assurance, and business teams of your organization.
You must always remember that the final goal of NLP in automation testing is to make your tests more accessible, intelligent, and aligned with your overall business goals.
Major Benefits Of NLP In Automation Testing
To further help you understand the role of natural language processing in modern automated test cases, let us divert our attention towards some of the major benefits that you can achieve by the inclusion of this parameter within your test environment:
1. Simplified Test Case Authoring
If you are using traditional automation test cases, there is a high possibility that you are manually creating all your automation tests by using any of your chosen programming languages. If so, then you must have realized that this entire process requires complex programming knowledge and will also limit the number of people who can take part in the test creation and execution process.
With the inclusion of NLP, even the non-technical members of the company can take part in the test creation process. These can include the business analysts, product owners, or even the manual testers, who don’t have any knowledge about programming languages.
2. Improved Test Coverage and Accuracy
Using the capabilities of NLP, you can analyze the requirement documents, user stories, or even the acceptance criteria for the web application. Depending on the information that you will receive in this process, the system can suggest missing or implicit Test cases.
The proper implementation of this parameter will help you to enhance test coverage, detect incomplete specifications, and implement early identification of potential bugs.
For example, if you have set a requirement for the application to send a confirmation e-mail after purchase, the NLP will go through scenarios for invalid e-mail addresses, server unavailability, or delay in the e-mail delivery.
3. Behavior-Driven Development (BDD) Enhancement
Behavior-driven development will heavily rely on using natural language constraints like Gherkin syntax to define application behavior. You can improve the power of behavior-driven development with the integration of natural language processing. This is possible due to the understanding of intent beyond syntax, mapping test scripts to code automatically, and suggesting improvements or inconsistencies.
Instead of relying on hard-coded test scripts, this model will dynamically interpret the user stories to reduce the overall maintenance overhead for your test environment.
4. Test Case Maintenance and Scalability
With the continuous evolution of your web application, the test cases will require regular updates so that you can add all the features and their required test cases. With the use of NLP, you can analyze the changes in user stories, documentation, or UI changes to recommend updating existing test cases, deprecation of obsolete tests, and generation of new test scenarios.
This involvement will be very important for ensuring the scalability and easy maintenance of your overall testing infrastructure.
Key Applications of NLP in QA Automation
Let us now divert our attention to some of the major areas in QA automation that can benefit with the inclusion of natural language processing:
1. Automated Test Script Generation
Using the power of natural language processing, you can convert functional requirements or user stories into automated test scripts. For this purpose, you can also rely on tools like Test.AI, Functionize, and Testim. This integration will help you understand the user intent and generate the scripts with the lowest human involvement.
2. Test Case Prioritization
Using natural language processing techniques like sentiment analysis or risk prediction, you can rank your test cases depending on the impact, criticality, or historical defect rates.
The above workflow will provide adequate information to the quality assurance teams so that they can prioritize all the critical areas of the application at the earlier phases of the testing process. This is a very important parameter to ensure that at least the basic functioning of the application is accessible at all instances.
4. Log Analysis and Anomaly Detection
In most cases, you must have already realized the test logs are highly complex and very critical. But with the integration of the natural language processing, you no longer have to worry about these.
This is because, with this model, you can find and summarize logs, detect anomalies, and correlate the logs across distributed systems to pinpoint the failures.
The entire workflow will have a very important role in reducing the overall time that you will be investing to find the root cause for any failure.
5. Intelligent Chatbots for QA
Finally, the quality assurance teams can use chatbots powered by natural language processing. These bots will answer queries related to the test status, documentation, and the entire defect tracking mechanism.
The advantage? You can improve the team collaboration and also reduce the dependency on QA leads or documentation teams. This process will also have a very important role in maintaining general awareness so that everyone in the team has a basic knowledge about the current status of the development project.
Best Practices for Implementing NLP in QA
Finally, let us divert our attention towards some of the best practices that we highly recommend the testers to add within the test environment while using natural language processing for quality assurance. These practices will not only help improve the quality and scalability of the approach but also have an impact on the application as a whole:
● Start Small and Scale Gradually
Since the entire concept of artificial intelligence and machine learning in software testing is completely new to almost the entire industry, we suggest you to start with a small non-critical aspect of the testing process for implementing the NLP model.
This approach will help you understand how your specific requirements and your test environment can benefit with the integration. Depending on the results that you achieve and the stakeholder approvals, you can scale accordingly.
● Use Hybrid Approaches
While you are starting to implement NLP-based automation testing, you should not eliminate the traditional rule-based test cases completely. Instead, we would suggest you to maintain a proper balance between NLP with rule-based systems. This approach will help you implement better accuracy and control in critical scenarios.
Moreover, while using any form of artificial intelligence, you must be very careful to have human supervision to ensure that all the outcomes and AI model analysis are supervised and as expected.
● Implement Cloud Testing
While you are slowly transitioning into the use of AI for software testing, we strongly recommend you to start investing in AI-based cloud testing platforms. This is because, with these platforms, you can integrate the capabilities of artificial intelligence while running all your test cases on real devices accessible through remote servers.
To learn more about the role of these platforms, let us use the example of LambdaTest as our reference point:
LambdaTest is an AI-native test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000+ browsers, OS combinations, and 5000+ real devices. With LambdaTest, you can also integrate automation frameworks like Selenium and Appium to initiate advanced processes like automated cross-browser testing and automated mobile app testing.
● Train Domain-Specific Models
We would strongly encourage the testers to customize the NLP models using domain documentations, defect logs, and test cases. If you are able to implement this process properly, you will be able to achieve higher precision depending on the customized requirements of your application.
● Monitor and Iterate
Although you have implemented artificial intelligence within your software testing, you should not forget to regularly monitor the performance of the test cases and their execution reports.
You should also regularly evaluate the performance of NLP-powered systems, gather feedback, and fine-tune the models depending on all the feedbacks received. With this approach, you can continuously improve the functioning of the model to ensure that it continues to keep up with the changing requirements of the market.
The Bottom Line
Based on all the areas that you went through in this article, we can easily see that natural language processing is changing the way in which you can approach automation testing. By incorporating all the strategies and practices that you mentioned in this article, you can bridge the gap between the technical and non-technical members in the QA process.
So, whether you are a QA engineer, product owner, or a CTO, understanding the power of natural language processing will be beneficial in multiple scenarios. We also suggest you to have an understanding of your application and the intentions for it so that you can customize the capabilities of NLP accordingly.
Want one final advice? Look out for all the upcoming innovations in this segment so that you can constantly incorporate them and provide nothing but the best end-user experience to your customers.