Successful implementation of test automation for functional testing requires thorough _______________ of test scenarios and requirements.
- Analysis
- Documentation
- Execution
- Planning
The successful implementation of test automation for functional testing necessitates thorough _______________ of test scenarios and requirements. Analyzing and understanding the testing requirements and scenarios is crucial to designing effective and comprehensive automated test cases that align with the application's functionality.
_______________ is a type of locator in Selenium that searches for elements based on their text.
- ID
- Link Text
- Name
- Text
The 'Link Text' locator in Selenium is used to find elements based on the visible text within hyperlinks. It is particularly useful for identifying and interacting with links containing specific text. This allows testers to locate elements without relying on the structure or other attributes, enhancing the flexibility and maintainability of Selenium scripts.
Which technique is used to identify test cases at the edges of input domains in Boundary Value Analysis?
- Equivalence Partitioning
- Exhaustive Testing
- Random Testing
- State Transition Testing
Equivalence Partitioning is used in Boundary Value Analysis to identify test cases at the edges of input domains by dividing the input into meaningful equivalence classes.
_______________ conditions are often defined in State Transition Testing to specify the state transitions.
- Entry and exit
- Initial and final
- Pre and post
- Trigger and action
State Transition Testing involves defining conditions that trigger state transitions. These conditions, often referred to as trigger and action conditions, specify when the system should move from one state to another. Identifying and testing these conditions help ensure the correct functioning of the system as it transitions between states.
Equivalence Partitioning helps to reduce _______________ by selecting representative test cases from each partition.
- Test Complexity
- Test Documentation
- Test Execution Time
- Test Redundancy
Equivalence Partitioning reduces test redundancy by selecting a single representative test case from each partition, ensuring that each group is adequately tested.
Equivalence Partitioning divides the input data into _______________ partitions.
- Disjoint
- Overlapping
- Randomized
- Sequential
Equivalence Partitioning divides the input data into disjoint partitions, ensuring that each partition is tested comprehensively. It simplifies testing and enhances test coverage.
_______________ testing focuses on assessing the system's ability to handle data inputs within specified ranges.
- Compatibility Testing
- Performance Testing
- Stress Testing
- Usability Testing
Stress testing assesses the system's ability to handle data inputs within specified ranges and under extreme conditions. It helps identify the system's breaking point.
During Exploratory Testing, testers create _______________ to guide their exploration.
- Test Cases
- Test Plans
- Test Scenarios
- Test Scripts
Testers create Test Cases during Exploratory Testing to guide their exploration. While the testing is unscripted, having a plan helps ensure that critical areas are covered, and findings are documented. Test Cases act as a framework for organized and effective exploration.
How does the Defect Lifecycle contribute to the overall quality of the software product?
- By eliminating the need for testing
- By preventing defects in the code
- By providing a systematic approach
- By quickly closing all reported defects
The Defect Lifecycle contributes to the overall quality of the software product by providing a systematic and controlled approach to managing defects. It ensures that identified issues go through a structured process, from identification to resolution and verification. This systematic approach helps in maintaining software quality by addressing defects in an organized manner, reducing the chances of overlooking critical issues during development.
What are some potential risks associated with relying on AI and Machine Learning models for functional testing?
- Difficulty in Model Interpretation
- Increased Testing Effort
- Lack of Understanding of Model Decisions
- Overreliance on Automation
Relying on AI and Machine Learning models in functional testing introduces risks such as a lack of understanding of model decisions. Models may make complex decisions that are challenging for humans to interpret, leading to potential blind spots in testing. Testers must be cautious and supplement AI-based testing with human expertise to ensure a comprehensive and reliable testing strategy.