Test Data Generation System Using Algorithmic Selection

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Solution Overview

Problem

In data-driven testing, generating test data efficiently and effectively is challenging due to the need for expertise in programming languages and software engineering, requiring users to provide specific input values, which is time-consuming and prone to human error.

Innovation Solution

A system and method that automatically generates test data by leveraging preestablished algorithms for software engineering, programming languages, and business use cases, allowing users to input only generic values, which are then applied to relevant algorithms to produce test data for target software, utilizing reduction and optimization techniques to focus and prioritize the generated data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users manually generate test data with expertise in programming languages and software engineering, then test data quality and relevance are improved, but time consumption and human error increase

Engineering Contradiction:

Improvetest data qualityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service test data generation by automatically selecting and applying appropriate testing algorithms based on user-provided generic inputs and data types, eliminating the need for users to manually craft test data with expert knowledge while maintaining high quality through algorithmic rigor

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Preestablished testing algorithms serve as intermediaries between user inputs and target software testing, translating generic user inputs into comprehensive test data sets through standardized software engineering, programming language, and business use case specific algorithms

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If users provide specific input values for test data generation, then test case precision is improved, but ease of operation deteriorates

Engineering Contradiction:

Improvetest case precisionVSAvoidease of operation

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

Users provide only partial input (generic values and data types) rather than complete specific test data, while the system performs excessive processing by automatically selecting algorithms and generating comprehensive test data sets that exceed minimal requirements

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs self-service by automatically selecting appropriate testing algorithms and generating precise test data without requiring users to provide specific input values, maintaining precision through algorithmic selection based on data type and testing objectives

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If comprehensive test data is generated covering all possible cases, then test coverage is improved, but device complexity increases

Engineering Contradiction:

Improvetest coverageVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The testing algorithm library is segmented into distinct categories (software engineering algorithms, programming language algorithms, business use case specific algorithms) that can be independently selected and applied based on testing requirements, managing complexity through modular organization

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system achieves universality by providing a single platform that handles multiple testing scenarios through preestablished algorithms covering software engineering principles, programming language constructs, and business use cases, eliminating the need for separate testing tools for different test types

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS9529699B2System and method for test data generation and optimization for data driven testing

Publication Date: 2016.12.27 WIPRO LTD

AI summary

A system, medium and method for automatically generating test data to be applied to test a target software code is disclosed. Input parameter data is received from a user via a displayed user interface, wherein the input parameter data is directed to a user selected data type, the data type being a Boolean, string, or integer. One or more preestablished stored testing algorithms is automatically selected based on the user selected data type and one or more values are applied to the selected one or more preestablished stored testing algorithms in accordance with the user selected data type. At least one set of test data from the one or more identified applicable testing algorithms is automatically generated, wherein the at least one set of test data generated from the identified testing algorithms can be used as inputs for testing the target software code.