Test Case Execution via NLP Token Classification
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Solution Overview
Problem
Conventional software testing processes require high manual efforts, skilled automation developers, and lengthy durations, involving complex automation test scripts and limited timelines, which restricts the efficiency and accessibility of test case execution.
Innovation Solution
A system and method utilizing Machine Learning and Natural Language Processing to classify test steps, generate output templates, and execute test cases dynamically using an Automation Engine, eliminating the need for tool-specific scripts and skilled developers, allowing manual testers to execute test cases in plain English.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automation test scripts are used, then test execution can be automated, but high manual efforts and skilled developers are required
Solution Approach 1:
The system enables test cases to execute themselves by automatically generating executable code from natural language descriptions. The NLP processor and code generator work autonomously to convert user-friendly test step descriptions into machine-executable test scripts without requiring manual intervention from skilled developers.
Solution Approach 2:
The patent replaces the mechanical process of manually writing and maintaining complex automation scripts with an intelligent system using NLP and code generation. Instead of testers manually creating scripts in specialized languages, the system automatically translates natural language test descriptions into executable code, substituting human manual work with automated intelligence.
2Ease of operation
If manual testing processes are used, then test cases can be executed without automation scripts, but high manual efforts and lengthy durations are consumed
Solution Approach 1:
The system performs preliminary actions by pre-generating executable test code from natural language descriptions before actual test execution. This preparation phase automatically creates the automation scripts that will be used during test running, eliminating the need for manual script writing while maintaining high execution efficiency.
Solution Approach 2:
The patent introduces an intermediary system consisting of the NLP processor and code generator that bridges the gap between simple manual test descriptions and complex automated execution. This intermediary automatically translates user-friendly test step descriptions into machine-executable code, enabling non-technical users to achieve automated testing without learning complex scripting languages.
3Extent of automation
If tool-specific automation languages are used, then automation test scripts can be created, but limited timelines and restricted accessibility are imposed
Solution Approach 1:
The system provides a universal interface for test automation that works across different testing tools and platforms. By accepting natural language descriptions as input and generating appropriate executable code for various automation frameworks, the system makes test automation accessible to users regardless of their familiarity with specific tool languages or platforms.
Solution Approach 2:
The patent changes the fundamental parameter of test script creation from requiring specialized knowledge of automation languages to using simple natural language. This parameter change transforms the accessibility of test automation from being restricted to skilled developers to being available to any user who can write basic English descriptions of test steps.
Data Source
AI summary
Disclosed is a system (102) for executing a test case. The system (102) comprises a memory (206) and a processor (202). The system (102) receives a test file in a predefined format. The test file comprises a test case comprising one or more test steps, test data and one or more expected results for execution. Further, each test step from the one or more test steps indicates an action to be performed for executing the test case. The system (102) generates one or more tokens by classifying text associated with the one or more test steps. The system (102) generates an output template associated with the test case based on analysing the one or more tokens. The system (102) executes the test case based on one or more controls associated with the test case in the output template. The one or more controls are dynamically identified from the output template.