Machine-Learning Software Testing Interface from Natural Language
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Solution Overview
Problem
Conventional software testing requires specialized expertise and manual coding, making it slow, error-prone, and inaccessible to less-technical individuals, and necessitates re-coding for changes.
Innovation Solution
A machine-learning enabled interface that interprets natural language inputs to derive software test attributes, automatically selects a script template, populates variable parameters, and executes the test without coding, using a trained NLP model to understand user inputs and generate a runtime test script.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional software testing methods are used, then testing can be performed with manual coding and specialized expertise, but the process becomes slow, error-prone, and inaccessible to less-technical individuals
Solution Approach 1:
A machine learning model serves as an intermediary between natural language input and test script generation. The model translates user-friendly natural language descriptions into structured test attributes and selects appropriate script templates, eliminating the need for users to directly code while maintaining testing functionality.
Solution Approach 2:
The manual coding process is replaced with an automated machine learning-based system. Instead of requiring users to mechanically write and modify code, the system automatically generates test scripts from natural language inputs, substituting the mechanical coding process with an intelligent automated system.
2Adaptability or versatility
If manual coding is used for software testing, then tests can be customized, but changes require re-coding which increases time and effort
Solution Approach 1:
The system dynamically adapts to user needs by accepting natural language descriptions that can be easily modified. When changes are needed, users simply update their natural language input rather than rewriting code, and the system regenerates the appropriate test scripts, providing dynamic adaptability without the overhead of re-coding.
Solution Approach 2:
The system changes the fundamental parameter of input from code to natural language. This parameter change allows users to modify test requirements by changing their language descriptions rather than modifying code structure, significantly reducing the time and effort required for test customization and changes.
3Reliability
If specialized expertise is required for software testing, then testing can be performed accurately, but it becomes inaccessible to less-technical individuals and increases operational complexity
Solution Approach 1:
The machine learning model acts as an intermediary that bridges the gap between non-technical users and the complex testing system. It handles the translation from simple natural language to structured test attributes, ensuring accurate test generation without requiring users to possess specialized testing knowledge or skills.
Data Source
AI summary
A computing environment can implement software testing based on attributes derived from natural language inputs. For example, a system can receive a natural language input describing a software test. The system can apply a trained machine-learning model to the natural language input to determine attributes of the software test. The system can select a predefined script template based on the attributes. The system can then populate variable parameters of the predefined script template using the attributes in the entry to create a runtime test script. The runtime test script can be configured to implement the software test, as customized based on the attributes. The system can provide the runtime test script to a running service. The running service can execute the software test consistent with the variable parameters.