Natural Language Processing for Software Testing

Anushree Chatterjee

The ways we communicate with technology today are quite different from what they used to be. From asking Siri about the weather to getting customer support via chatbots to using Google Translate while traveling, NLP is behind all these interactions. 

Some more examples of NLP in action are email services like Gmail that automatically filter spam and categorize emails into primary, social, and promotional tabs. Platforms like Twitter and Facebook use NLP to detect inappropriate content, monitor sentiment around topics or brands, and tailor content recommendations to users. Apps like Apple’s Voice Memos, Google’s Voice Typing and transcription services use NLP to convert spoken language into written text. Tools like Grammarly and the spell checker in Microsoft Word use NLP to analyze and correct grammar, spelling, and style in written text. The list goes on…

When it comes to software testing, you can see NLP being incorporated into test automation tools. However, before delving into that, let’s understand NLP better.

What is NLP?

Natural Language Processing (NLP) is like teaching a computer to understand and respond to human language. Imagine trying to explain a complex task to a friend who doesn’t speak your language. It would be difficult, right? NLP helps bridge this gap between humans and computers. 

NLP is a field of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The primary goal of NLP is to enable computers to understand, interpret, and respond to human language in a meaningful and useful way.

Common techniques involved in NLP

Think of these techniques as tools in a toolbox. Each one helps NLP systems get closer to understanding human language in a way that’s useful for various applications. 

We can look at these techniques as fulfilling different purposes, like processing the text, analyzing it, and then generating responses. Here’s a list of the various NLP techniques that are used daily by the apps and websites we interact with without us even realizing it.

• Tokenization: Breaking down text into individual words or tokens. This facilitates further analysis by making it easier to handle and manipulate text.
• Stop word removal: Eliminating common words like “the”, “and”, “is” that don’t add much meaning.
• Stemming and lemmatization: Reducing words to their root form like “running” becomes “run”. This normalizes text making it easier to analyze by reducing inflectional forms. 
• Part-of-speech tagging: Identifying the grammatical role of words like noun, verb, adjective, etc.
• Named Entity Recognition (NER): Recognizing and classifying entities like people, organizations, locations, etc. This is useful for extracting important information from text such as in news articles or legal documents.
• Parsing (syntactic parsing): Analyzing the grammatical structure of a sentence to understand its syntax. This helps in understanding the relationships between words in a sentence which is crucial for deeper text analysis.
• Sentiment analysis: Determining the emotional tone of a text like positive, negative or neutral. It is used for social media monitoring, customer feedback analysis and market research to gauge public opinion.
• Text classification: Categorizing text into predefined categories like spam or not spam.
• Topic modeling: Discovering underlying topics within a collection of documents.
• Machine learning: Learning patterns from data with the help of algorithms and make predictions.
• Language modeling: Predicting the probability of a sequence of words. This is used in predictive text, autocomplete and text generation.

Why is NLP important in software testing?

In software testing NLP is used for generating test cases and managing test data to detecting defects and analyzing user feedback. With this additional help that NLP gives to testers, no wonder it is gaining popularity.

Here are some of the main advantages of adopting NLP:

• Automation: NLP, like other technology, takes care of the burdensome tasks. It takes care of various software testing activities, thus saving time and resources.
• Automated test case generation: Process natural language requirements and specifications to automatically generate test cases. This saves time and reduces human error.
• Improved test coverage: By analyzing requirements comprehensively, NLP can identify potential test scenarios that might be missed in manual testing, leading to better test coverage.
• Efficient test maintenance: Update test cases according to changing requirements. This reduces maintenance efforts and ensures test cases remain relevant.
• Enhanced test reporting: NLP can analyze test results and generate comprehensive reports that highlight trends, patterns, and areas for improvement.
• Faster bug identification: Process error logs and user feedback to quickly identify and prioritize bugs.
• Better understanding of user needs: Get insights into user expectations and help improve product quality by analyzing user reviews and feedback.
• Improving communication: Translate technical jargon into plain language which makes it easier for non-technical stakeholders to understand test results and issues.
• Assistance to usability and exploratory testing: With this technology you can analyze user feedback, reviews and sentiment to identify usability issues and areas for improvement. It can also help understand and summarize large volumes of documentation and user feedback to guide exploratory testing efforts.

What can NLP do for a tester?

You must have heard various claims by test automation tools saying that they can take care of test case generation, get tests to heal by themselves if they fail, or generate comprehensive test reports. Sounds too good to be true? But this is possible because these tools have harnessed the power of AI, and NLP plays a huge role here. 

Here are various aspects in which NLP aids testers:

Test case generation and management

• Requirement analysis: Extract requirements from documents like user stories, use cases and functional specifications. By understanding the language NLP can also identify test conditions, expected results and test data.
• Test case creation: Automatically generate test cases in a structured format based on extracted requirements.
• Test case maintenance: Update test cases as requirements evolve. This reduces manual effort and ensures test coverage.

Test data generation

• Data profile creation: Analyze existing data to create data profiles and identify patterns and relationships.
• Synthetic data generation: NLP can generate synthetic test data that mimics real-world data based on data profiles which helps protect sensitive information.

Test automation

• Natural language test scripts: NLP can convert natural language test cases into executable test scripts, simplifying test automation.
• Self-healing tests: Analyze test failures and automatically modify test scripts to adapt to changing UI elements which in turn improves test stability.

Test result analysis

• Defect identification: NLP can analyze test results to identify potential defects by recognizing patterns and anomalies.
• Test report generation: Generate comprehensive test reports that summarise test results, identify trends, and provide actionable insights.
• Root cause analysis: It can help pinpoint the root cause of defects by analyzing test failures and logs. This speeds up the debugging process.

Test environment management

• Environment provisioning: Can understand environment requirements from natural language descriptions and automatically provision test environments.
• Configuration management: Analyze configuration files and logs to identify inconsistencies or potential issues.

Requirements analysis

• Requirement validation: You can check for ambiguities, inconsistencies and missing requirements in natural language documents with NLP.
• Requirement traceability: NLP can establish traceability between requirements, test cases and defects which helps ensure comprehensive testing.

Examples of NLP in action

• Automating test case generation: Given a user story like “As a user I want to be able to log in to the system with my username and password,” NLP can generate test cases for successful login, invalid credentials, password recovery, etc.
• Analyzing test results: NLP can identify common error messages in test logs and suggest potential defects related to specific functionalities.
• Generating test data: For a banking application NLP can analyze existing customer data to generate synthetic customer profiles with realistic information for testing. Here is an article Automated Testing in the Financial Sector.
• Improving test automation: NLP can help create self-healing test scripts that can handle dynamic UI changes which reduce test maintenance efforts.

Test Automation tools using NLP

AI has taken the software testing industry by storm, guiding sails toward smart testing systems that reduce manual labor. This form of automation not only takes care of mundane, repetitive tasks but also has the potential to “think.” Various aspects of testing can be automated (as we saw in the above section) with the help of AI. 

Though many tools claim perfect scores, very few are able to deliver, especially since software is now at the center of business operations across industries. This leads to higher demands, shorter time to market, and constant customer scrutiny. 

One such tool that passes with flying colors is testRigor.

NLP through testRigor

The closest you will find to a human emulator, testRigor is one of those few tools that delivers what is promised. When you log into this cloud-based testing platform, you will see that writing test cases here is very easy. Just use plain English statements to tell testRigor what you want it to do and where you want it to click, and Voila! It does it. Here’s an example:

login
click on “Sales Dashboard”
check that page contains “Welcome John”

You can also utilize testRigor’s generative AI feature that creates fully functional test cases for you if you provide a comprehensive description of what you want to do. 

testRigor’s powerful engine is able to comprehend and execute the statements. Another great use of NLP that testRigor implements is the synonym match. So tomorrow, if “Sales Dashboard” changes to “Sales Board”, then testRigor can still identify it. testRigor does not rely on implementation details of UI elements, which makes testRigor test cases resilient to flakey runs and reduces test maintenance. Besides these, testRigor offers other self-healing capabilities that give you stable and reliable test runs. 

There are many more features that testRigor offers that use AI to simplify automated testing. 

Conclusion

NLP involves transforming human language into a format that computers can understand, analyzing it to extract meaning, and then using that understanding to perform various tasks. Its applications are seen across industries, in day-to-day lives, and even in software testing. With the right test automation tools you can leverage the best of NLP to software testing.

FAQs

Can NLP be used for both manual and automated testing?

Yes, NLP can enhance both manual and automated testing. For manual testing, it aids in analyzing requirements and user feedback. In automated testing, it can generate test cases, maintain test scripts, and analyze logs and errors.

What is the future of NLP in software testing?

The future of NLP in software testing is promising, with advancements in AI and machine learning leading to more sophisticated and accurate NLP models. These improvements will further automate and enhance testing processes, making them more efficient and effective.

What are the challenges of using NLP in software testing?

Challenges include handling the variability and complexity of natural language, ensuring accuracy in text interpretation, and integrating NLP tools with existing testing frameworks. Additionally, training NLP models requires significant data and computational resources.

“We spent so much time on maintenance when using Selenium, and we spend nearly zero time with maintenance using testRigor.”

Keith Powe VP Of Engineering - IDT