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Model Based Testing in Software Testing

Last Updated : 06 Mar, 2024
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Prerequisites: software-testing

Model-based testing is nothing but a simple testing technique in which we get different test cases that are described by the model. In this type, the test cases are generated via both online and offline test case models.  

In this case by considering the testing technique functionally we find out the model-based test cases. For checking the functionality of the software, the unit testing is not sufficient for this case so this is considered.

Significance of Model-Based Testing

  1. Early Defect Detection: Model-Based Testing (MBT) makes it possible for testers to find problems during the requirements or design phases by using model validation. By doing this, flaws are kept from spreading to more expensive fixing development phases.
  2. Lower Maintenance Costs: Since test cases are derived from models, any modifications to the system can immediately update the appropriate test cases, which in turn can reflect any changes made to the models. This lessens the work and expense of maintaining test cases, particularly in complex and large-scale systems.
  3. Reusable Test Assets: Models and test cases developed during the software development lifecycle can be utilized again for regression testing. This guarantees uniformity in testing procedures across projects and helps optimize the return on investment in testing efforts.
  4. Encouragement of Agile and DevOps Methods: Because it facilitates quick feedback loops and continuous testing, it works well with Agile and DevOps approaches. As part of the CI/CD pipeline, test cases can be automatically developed and run, giving developers quick feedback and guaranteeing the quality of deliverables.
  5. Enhanced Test Coverage: It generates test cases from models that describe all potential system behaviors and scenarios, assisting in ensuring thorough test coverage. This aids in the early detection of possible flaws throughout the development process.

Types of Model-Based Testing

  • Statecharts: These are an expansion of FSMs that enable complicated transitions, parallelism, and hierarchical state representation. They are frequently used to simulate the behavior of reactive systems, like embedded systems and user interfaces.
  • Markov Models: These systems display probabilistic behavior, with state changes taking place according to probabilistic rules. They are employed in system performance and reliability analysis as well as modelling stochastic processes.
  • Decision Tables: Decision Tables are a condensed, tabular method of expressing intricate decision reasoning. They are frequently utilized in rule-based systems and business logic validation, and they are helpful for modelling systems having conditional behavior.
  • Entity-Relationship Diagrams (ERDs): These diagrams show how different entities in a database schema are related to one another. They are frequently employed in database design for showing the relationships and data structure between various entities.
  • Control Flow Graphs (CFGs): CFGs show the order in which the code is executed, illustrating the control flow of a program. They are employed in test case generation, coverage analysis, and programme behavior analysis.
  • Data flow diagrams (DFDs): These show how data moves through a system with an emphasis on the entry, processing, and output of data. They are helpful in determining data dependencies and confirming that data transformations in software systems are accurate.
  • Unified Modelling Language (UML) diagrams: It offers a common notation for expressing different software system components. Use case diagrams show how users and systems interact, whereas activity diagrams show how control moves across a system.

Advantages of Model-Based Testing

  1. Efficiency: The automation efficiency is so much higher in this type and the higher level also be acquired by the model.
  2. Comprehensive testing is also possible in this type and the changes that have been made can be easily tested by model.
  3. Different types of machines like finite state machines, unified model diagrams, and state charts are mostly taking part in this testing technique.
  4. By reducing the cost of the process available in this type. Simultaneously many numbers of processes are running together for performance increase.
  5. The defects that are made in the beginning stage are identified and the defect counts increase accordingly the testing undergoes in a progressing manner.

Disadvantages of Model-Based Testing

  1. For testing purposes system always needs formal specifications and the changes are made according to different sets in a combined manner.
  2. To understand the concept is so much difficult for the user and also for utilization. So, the learning curve of the model will be more i.e. the biggest failure of the model.
  3. To overcome this situation, the model should be thoroughly improvised and trained.

Real Case Scenario of a Model

When a user is ready to go through the web application then the user has multiple sections like Sign In, forgot password, and reset password options i.e. total 3 fields there to enter into the home page so that case is only for one user and when considering for model-based multiple users some permutation and combinations can be used to testing the product in a model type. So, the state transition diagrams are involved to fulfil the requirements of the user.  

 Multiple states with multiple transitions make it possible to reduce the complexity of the task that has been performed by different permutation and combination techniques. Validation of the test cases and state transition diagrams are created automatically and provide better solutions for many users present in a queue for requesting access to the specific model.

Conclusion

Model-based testing is an approach to evolutionary testing. The testers are involved in the testing type to form mental models that are coming on the paper for better readability and reusability of the product under testing. In the past study, the testing was manual, and automation for the recent study model-based testing came to market.


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