How NLP-based automation could be a groundbreaking technology in the automation landscape.
NLP is a interdisciplinary field combining linguistics, computer science, information engineering, and artificial intelligence. Its primary focus is on enabling computers to process and analyze vast quantities of natural language data effectively.
Three Core Components of NLP-based Applications:
1- Understanding
2- Processing
3- Generation
To create effective NLP algorithms, it is crucial to comprehend these three core components. Each component carries out specific tasks, such as building vocabulary, creating meaningful statements, and generating context. While we won't delve into the details of each component here, let's explore how NLP can be applied to automation.
The Challenges of Testing
Despite the emergence of more efficient software development tools, manual test case creation remains prevalent. This time-consuming process poses several problems:
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Human error is inevitable, particularly among inexperienced testers who may create ambiguous or incorrect tests.
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The same tests often cannot be used in regression testing if application requirements change, necessitating the creation or modification of test cases.
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Testers' time constraints make achieving 100% efficiency in regression testing impossible, increasing the likelihood of bugs and system failures.
These issues can adversely affect a product's competitiveness, potentially leading to revenue loss. Test automation can help accelerate testing routines and reduce errors. NLP can further enhance the efficiency of test automation.
Incorporating NLP in Test Automation
Testers develop test cases based on customer requirements derived from user stories. To enable NLP-based tools to generate accurate test cases, testers must provide the following details:
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User story – includes end-user requirements and feature descriptions.
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Acceptance criteria – outlines how the software must function to meet the provided requirements.
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Test scenario description – depicts user-product interactions to generate the test case.
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Dictionary – consists of keywords used by the program to create test cases.
NLP techniques analyze the information, process it into frames, and convert it into unified modeling language (UML). UML, a software engineering modeling language, transforms requirements into diagrams and connections. The result is an automatically generated test case.
Pros and Cons of NLP in Test Automation
As with any method, using NLP in test automation has its advantages and disadvantages:
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Test scenario descriptions must contain all necessary information; otherwise, the results may be inaccurate or unworkable.
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Consistent language and writing style must be maintained in test scenario descriptions and user stories for accurate interpretation by the software.
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Learning to use the system, particularly when writing test scenario descriptions, takes time.
However, NLP-based techniques offer significant benefits:
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Nearly 100% test coverage.
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Reduced time spent on test creation.
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Increased test productivity and reusability.
Is NLP Worth It? Our Verdict
Integrating NLP into automated testing is a win-win. Test cases can be developed more quickly and with less effort, leading to faster time-to-market and improved quality – both crucial for customer satisfaction.
Even if a product owner modifies a feature, making amendments becomes more manageable, resulting in lower test case redundancy. Overall, the product will be tested more efficiently.