A Journey through Pros, Cons, and Adaptation
In the dynamic realm of software testing methodologies, Model-Based Testing (MBT) has emerged as a promising approach, offering a systematic way to design and execute artefacts for software and system testing. This article delves into the practical experiences of a QA team that embraced MBT, unravelling its pros, cons, and the challenges encountered in a real-world scenario.
Understanding Model-Based Testing (MBT)
At its core, MBT leverages model-based design to represent the desired behaviour of a System Under Test (SUT), testing strategies, and the test environment. The team embarked on this journey with the aim of building a smaller application capable of generating expected models/results, which could then be compared with the outputs of their larger application.
Pros of Model-Based Testing
1. Easy Test Case Addition:
- The team found that adding new test cases became a breeze. Simple changes to inputs triggered automatic generation of the updated model, streamlining the testing process.
2. Lightweight Business Logic Model:
- MBT facilitated the creation of a lightweight model of the business logic, ensuring comprehensive coverage of business requirements without unnecessary complexity.
3. Handling Complex Scenarios:
- Changing input data allowed the team to unintentionally create complex scenarios. MBT’s flexibility made it easy to cover intricate and unexpected testing scenarios.
4. Early Issue Identification:
- Developers gain the benefits of identifying issues without running the heavyweight main application, enhancing efficiency in the debugging and problem-solving process.
5. Maintenance Efficiency:
- With MBT, the team discovered that updating the model’s code was sufficient; there was no need to continually update expected results in automated tests.
Cons of Model-Based Testing
1. Dependency on Quality Requirements:
- The effectiveness of MBT relies heavily on well-defined and confirmed business requirements. The team faced challenges when requirements were unclear or subject to frequent changes.
2. Skill Requirement:
- Implementing MBT demands QA professionals with robust programming skills or the active involvement of developers in the model implementation process.
3. Unit Testing Overhead:
- Covering the Model Code with unit tests introduced additional work for QA or developers, requiring meticulous attention to ensure the reliability of the testing framework.
4. Challenge with Requirement Changes:
- Complex changes in requirements sometimes demanded a complete rewrite of the model to generate valid expectations.
5. Maintenance Challenges:
- Rapidly changing requirements posed a significant challenge in maintaining the MBT approach, requiring constant adaptation to stay aligned with evolving project needs.
Real-World Scenario: Navigating Challenges with MBT
The team’s decision to adopt MBT came from well-defined customer requirements, expectations of handling extensive input data sets, and a desire to quickly generate expected results without executing the main application extensively. Developers leveraged a smaller model to generate expected results for debugging, based on issues identified by the QA team during the implementation.
mplementation and Initial Success
The initial implementation showcased success in debugging with a smaller data set, instilling confidence in the team’s ability to efficiently handle the data transformation process. Results were very accurate and according to the initial Customer’s requirements.
Challenge: Unexpected Requirement Changes
Despite initial success, the team encountered unexpected challenges. Incorrect initial requirements, missing functionalities, and a lack of understanding on the customer’s part led to a reevaluation of the main application architecture.
Ongoing Struggles with Dynamic Requirements
The QA team found themselves in a continuous cycle of updating the Model Code to accommodate dynamic changes in requirements during each sprint.
Learning from Challenges
Manual verification became a crucial step in uncovering discrepancies in end results, leading to increased efforts and personnel for verification and code changes.
Test Framework Transition: A Necessary Evolution
The QA team has faced with the complexity and constant changes in the existing test framework, the team made the decision to implement a new, clearer framework without using MBT to streamline the testing process.
Alternative Approach and Lessons Learned
Adopting a simpler test framework for a different customer’s data transformation proved to be a time-saving and efficient alternative. The team learned the importance of simplicity and clarity, especially when onboarding new team members.
Conclusion
Model-Based Testing offers a powerful approach to software testing, providing advantages in test results generation, early issue identification, and maintenance efficiency. However, as demonstrated in this real-world scenario, challenges arise when dealing with dynamic requirements and complex changes in the Model. The key takeaway is the need for adaptability and a willingness to evolve testing strategies in response to the ever-changing landscape of software development.
In conclusion, the journey of this QA team underscores the importance of striking a balance between the benefits and challenges of Model-Based Testing, ensuring that the chosen approach aligns with project dynamics and remains resilient in the face of evolving requirements.
