In the changing world of software development, innovation continually shapes our interaction with technology. These languages fundamentally alter testing practices, offering testers an intuitive and seamless means of communication with automation frameworks and tools.
Democratizing Test Automation through Natural Programming Languages:
While conventional testing automation tools wield considerable power, they require specialized skills and knowledge. Enter natural programming languages in testing designed to bridge the chasm between technical complexity and user-friendly interfaces. These languages strive to simplify the testing process by harnessing human language, empowering testers across all skill levels to engage effortlessly with automated testing scenarios.
Illustrations of Natural Programming Languages in Testing Automation:
AccelQ stands out as a prominent contender in natural programming languages for testing. Its distinctive NPL (Natural Programming Language) approach permits testers to compose test cases in plain English while retaining the precision indispensable for automated testing. As an example:
Verify home page contains "Welcome to our website"
This intuitive language renders testing accessible to even non-technical team members, enhancing collaboration.
Functionize is a cutting-edge platform that leverages NLP for testing automation. With its AI-powered testing capabilities, testers can articulate test scenarios using natural language. For instance:
Navigate to the login page Enter valid credentials and click the login button Verify successful redirection to the dashboard
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Cucumber presents another influential tool that endorses natural language testing. It employs Gherkin, a business-readable language, to articulate application behaviors in a universally understandable manner. For instance:
Given the user is on the login page When they enter valid credentials Then they should be redirected to the dashboard
Selenium and Natural Language Bindings:
Automation frameworks like Selenium offer natural language bindings that simplify test scripting. For instance, utilizing the WebDriver library in Python:
Such bindings augment script readability and make test scripts more user-friendly.
The Differences Between Natural Programming Languages and Natural Language Processing
Let us now explore the difference between Natural language processing (NLP) and Natural Programming Languages (NPL)
Natural language processing (NLP) and Natural Programming Languages (NPL) are related concepts that serve different technological purposes. Let’s clarify the distinctions between the two:
Natural Language Processing (NLP):
Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and interact with human language in a meaningful and valuable way. NLP involves the development of algorithms, models, and systems that allow computers to process and analyze text or speech data in natural language. NLP encompasses text classification, sentiment analysis, language translation, speech recognition, and more.
Natural Programming Languages (NPL):
Natural Programming Languages (NPL) is a subset of natural language processing designed to simplify writing computer programs or test scripts using human-like language. NPL allows users, mainly those not proficient in traditional programming languages, to interact with software systems and automation tools using familiar expressions and sentences. These languages use a syntax that resembles human language, making it easier for non-programmers to write code.
While both involve working with human language, NLP focuses on enabling computers to understand and interact with human language. In contrast, NPL programming or scripting using language is more intuitive and closer to natural human expression.
Anticipating the Future: Understanding the Potential of NPL
The arrival of natural programming languages for testing ushers in a new era of collaboration between developers, testers, and business stakeholders. In forthcoming blogs, we will delve into specific technologies such as AccelQ and Functionize, highlighting their impact on automation testing. We’ll explore how testers seamlessly translate human language requirements into automated test cases, ensuring precision and efficiency.
Join Us on this Journey:
In the upcoming blogs, we will embark on an enthralling journey to unearth the untapped potential of natural programming languages in testing automation. We’ll uncover real-world scenarios, success narratives, and guides to empower you to leverage these languages for testing excellence.