Edited from: http://www.kiyun.com/Show/fangzhen/cid/2/id/170.html
Model-based test case design tool (AutoTCG), using a graphical model-based test engine, suitable for business-critical, mission-critical or safety-critical functional testing. Testers can choose the cause-and-effect graph model, combination pairing model or business process model to complete the test design, and automatically generate use cases; AutoTCG can be integrated with Selenium, ETest and other mainstream test tools to realize automated test execution, which can effectively improve test efficiency and quality, and speed up testing. Test progress, reduce delivery costs, and better ensure product quality.
1. Causal graph model
Application Scenario
With the help of graphics, the causality diagram method can intuitively analyze and express various combinations of inputs, constraints, and output results under each combination condition. Using the causal graph method, not only errors in input and output can be found, but also incompleteness and ambiguity in specifications can be found.
The main function
**1. ** Visual creation of causal graph model
◎ Use visualization to convert functional specifications into graphical representations
◎ Automatically diagnose errors in the model and discover potential problem areas at any time
◎ Manage and maintain the relationship between demand items and causality graph nodes
◎ Complete support for constraint rules (mutual exclusion, inclusion, uniqueness, requirements, shielding)
2 **、Automatically generate decision table**
◎ Automatically generate decision tables to design minimum number of test cases for maximum functional coverage
◎ Automatically identify the sensitive relationship between input conditions and output results
◎ Help users find observable points and improve program testability
◎ Automatically generate use case analysis matrix view
3 **、Use case optimization and coverage analysis**
◎ The causal sensitive relationship in the test case is clear at a glance
◎ Automatically select the optimal set of use cases to ensure the best coverage within a limited time
◎ Comprehensive display of strong and weak coverage of use cases
◎ Set test completion criteria and automatically filter the most suitable set of use cases
2. Combined pairing model
Application Scenario
Combinatorial pair testing is a black-box test design technique that provides one hundred percent test coverage; where test cases are designed to execute all possible discrete combinations of each pair of input parameters; useful for testing involving multi-parameter applications help.
The main function
**1. ** Visual establishment of combined pairing model
◎ Simple and fast design combination matching model
◎ Multi-layer nesting of self-sustaining sub-models, decomposing complex logic
◎ The pairing factor can be freely selected
◎ Support setting constraints between parameters
**2. ** Input parameter value design
◎ Automatically generate boundary values, random numbers, self-increment, self-decrement and other values through a certain value interval
◎ Data value accuracy can be set arbitrarily
◎ Support setting the weight coefficient of a specific value
◎ Support automatic import of parameter definitions and values from protocol definitions
**3. ** Automatically generate use cases
◎ Adopt optimized combination and matching algorithm to automatically generate use cases
◎ Reach the set combined coverage target with the minimum number of test case sets
◎ Support for generating reverse test cases
◎ Automatically analyze model settings and give friendly prompts in real time
3. Process-based model
Application Scenario
Create a visual test model based on the business process, comprehensively apply algorithms such as path search, deep combination, pair combination, and constraint solving, and automatically generate test data and test execution steps to achieve scientific and comprehensive test coverage.
The main function
**1. ** Visual modeling
◎ Use standard BPMN2.0 symbols to build test models visually
◎ Simple and convenient model designer to quickly build test models
◎ Real-time model automatic inspection function, find problems at any time
◎ Support multi-layer nesting of sub-models to decompose complex business logic
2. Input parameter design
◎ Configure input parameters step by step to facilitate manual analysis
◎ The input parameter constraint setting supports calculation expressions, and the scope of application is wider
◎ Automatic identification of parameter types, easier to use
◎ Automatically analyze input parameters to locate design problems early
3. Automatically generate test cases
◎ Adopt path depth coverage algorithm to ensure full coverage of execution steps
◎ Combination matching algorithm is adopted to ensure the scientific coverage of input parameter combinations
◎ Adopt path reduction algorithm to ensure optimal coverage of test cases
◎ Automatically solve constraints to ensure the validity of each test case
4. Execute code output and test execution
◎ Automatically generate python, lua, javascript, c#, c++ and other formats of code
◎ Support code generation plug-in customization
◎ Automatically generate test case execution directory
◎ Support integration with ETest, Selenium and other testing tools