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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test Analysis and Design | 30% | - Experience-based test techniques - Black-box test techniques - Test techniques overview - White-box test techniques - Collaboration-based approaches |
| Topic 2: Test Tools and Automation | 2.5% | - Tool support for testing - Benefits and risks of automation |
| Topic 3: Managing Test Activities | 22.5% | - Risk-based testing - Configuration management - Test planning - Defect management - Monitoring, control and completion |
| Topic 4: Testing Throughout the Software Development Lifecycle | 15% | - Testing in SDLC contexts - Maintenance testing - Test levels - Test types |
| Topic 5: Fundamentals of Testing | 20% | - Why testing is necessary - Essential skills and good practices - What is testing - Testing principles - Test activities, testware and roles |
| Topic 6: Static Testing | 10% | - Review process and types - Static testing basics |
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NEW QUESTION # 49
A program is used to control a manufacturing line (turn machines on and off. start and stop conveyer belts, add raw materials to the flow. etc.). Not all actions are possible at all times. For example, there are certain manufacturing stages that cannot be stopped - unless there is an emergency. A tester attempts to evaluate if all such cases (where a specific action is not allowed) are covered by the tests.
Which coverage metric will provide the needed information for this analysis?
Answer: D
Explanation:
Branch coverage is a type of structural coverage metric that measures the percentage of branches or decision outcomes that are executed by the test cases. A branch is a point in the code where the control flow can take two or more alternative paths based on a condition. For example, an if-else statement is a branch that can execute either the if-block or the else-block depending on the evaluation of the condition. Branch coverage ensures that each branch is taken at least once by the test cases, and thus reveals the behavior of the software under different scenarios. Branch coverage is also known as decision coverage or all-edges coverage.
Branch coverage is suitable for testing the cases where a specific action is not allowed, because it can verify that the test cases cover all the possible outcomes of the conditions that determine the action. For example, if the program has a condition that checks if the manufacturing stage can be stopped, then branch coverage can ensure that the test cases cover both the cases where the stage can be stopped and where it cannot be stopped.
This way, branch coverage can help identify any missing or incorrect branches that may lead to undesired or unsafe actions.
The other options are not correct because they are not suitable for testing the cases where a specific action is not allowed. Code coverage is a general term that encompasses various types of coverage metrics, such as statement coverage, branch coverage, data flow coverage, etc. Code coverage does not specify which type of coverage metric is used for the analysis. Data flow coverage is a type of structural coverage metric that measures the percentage of data flow paths that are executed by the test cases. A data flow path is a sequence of statements that define, use, or kill a variable. Data flow coverage is useful for testing the correctness and completeness of the data manipulation in the software, but not for testing the conditions that determine the actions. Statement coverage is a type of structural coverage metric that measures the percentage of statements or lines of code that are executed by the test cases. Statement coverage ensures that each statement is executed at least once by the test cases, but it does not reveal the behavior of the software under different scenarios.
Statement coverage is a weaker criterion than branch coverage, because it does not account for the branches or decision outcomes in the code. References = ISTQB Certified Tester Foundation Level (CTFL) v4.0 syllabus, Chapter 4: Test Techniques, Section 4.3: Structural Testing Techniques, Pages 51-54.
NEW QUESTION # 50
Which of the following statements about static testing and dynamic testing is TRUE?
Answer: A
Explanation:
Static testing, such as code reviews and static analysis, is particularly effective at identifying issues related to code structure and modularization. These techniques allow for the inspection of the code without executing it, making it easier to spot problems related to how the code is organized. Dynamic testing, on the other hand, focuses on the execution of code and is better suited for identifying runtime issues but does not easily reveal structural problems. The ISTQB CTFL Syllabus v4.0 highlights the strengths of static testing in uncovering such structural issue
NEW QUESTION # 51
A requirement specifies that if the total amount of sales (TAS) made during the year by a corporate seller is 300,000 or more, the bonus that must be paid to the seller is 100% of a certain amount agreed upon at the beginning of the year. The software contains a fault as it implements this requirement with the decision "IF (TAS = 300,000)" instead of "IF (TAS >= 300.000)". The application of the 3-value boundary value analysis to this problem consists of the following three test cases (TAS is an integer variable):
TC1 = 299,999 TC2=300,000 TC=300,001
Which of the following statements is TRUE?
Answer: A
Explanation:
The fault in the software is that it checks only for TAS = 300,000 rather than TAS >= 300,000.
This means the bonus is paid only if the TAS is exactly 300,000, not when it exceeds 300,000 as intended.
With boundary value analysis, we test the values just below, at, and just above the boundary condition:
TC1 (TAS = 299,999): No bonus is expected, so this test case would not expose the fault.
TC2 (TAS = 300,000): The software would pay the bonus correctly according to its faulty condition, so this would not expose the fault either.
TC3 (TAS = 300,001): According to the requirement, the bonus should be paid, but the software's faulty condition will not pay the bonus because it only checks for TAS exactly equal to 300,000.
Therefore, TC3 would highlight the fault by demonstrating that the bonus is not paid when TAS is greater than 300,000, which contradicts the requirement.
NEW QUESTION # 52
Which of the following statements about how different types of test tools support testers is true?
Answer: A
Explanation:
The support offered by a performance testing tool is often leveraged by testers to run load tests, which are tests that simulate a large number of concurrent users or transactions on the system under test, in order to measure its performance, reliability, and scalability. Performance testing tools can help testers to generate realistic workloads, monitor system behavior, collect and analyze performance metrics, and identify performance bottlenecks. The other statements are false, because:
A test data preparation tool is a tool that helps testers to create, manage, and manipulate test data, which are the inputs and outputs of test cases. Test data preparation tools are not directly related to running automated regression test suites, which are test suites that verify that the system still works as expected after changes or modifications. Regression test suites are usually executed by test execution tools, which are tools that can automatically run test cases and compare actual results with expected results.
A bug prediction tool is a tool that uses machine learning or statistical techniques to predict the likelihood of defects in a software system, based on various factors such as code complexity, code churn, code coverage, code smells, etc. Bug prediction tools are not used by testers to track the bugs they found, which are the actual defects that have been detected and reported during testing. Bugs are usually tracked by defect management tools, which are tools that help testers to record, monitor, analyze, and resolve defects.
A continuous integration tool is a tool that enables the integration of code changes from multiple developers into a shared repository, and the execution of automated builds and tests, in order to ensure the quality and consistency of the software system. Continuous integration tools are not used by testers to automatically generate test cases from a model, which are test cases that are derived from a representation of the system under test, such as a state diagram, a decision table, a use case, etc. Test cases can be automatically generated by test design tools, which are tools that support the implementation and maintenance of test cases, based on test design specifications or test models. Reference: ISTQB Certified Tester Foundation Level (CTFL) v4.0 sources and documents:
ISTQB® Certified Tester Foundation Level Syllabus v4.0, Chapter 3.4.1, Types of Test Tools ISTQB® Glossary of Testing Terms v4.0, Performance Testing Tool, Test Data Preparation Tool, Bug Prediction Tool, Continuous Integration Tool, Test Execution Tool, Defect Management Tool, Test Design Tool
NEW QUESTION # 53
Following a risk-based testing approach you have designed 10 tests to cover a product risk with a high-risk level. You want to estimate, adopting the three-point test estimation technique, the test effort required to reduce the risk level to zero by executing those 10 tests. You made the following three initial estimates:
* most optimistic = 6 person hours
* most likely = 30 person hours
* most pessimistic = 54 person hours
Based only on the given information, which of the following answers about the three-point test estimation technique applied to this problem is true?
Answer: C
Explanation:
The three-point test estimation technique is a method of estimating the test effort based on three initial estimates: the most optimistic, the most likely, and the most pessimistic. The technique uses a weighted average of these three estimates to calculate the final estimate, which is also known as the expected value. The formula for the expected value is:
Expected value = (most optimistic + 4 * most likely + most pessimistic) / 6 Using the given values, the expected value is:
Expected value = (6 + 4 * 30 + 54) / 6 Expected value = 30 person hours However, the expected value is not the only factor to consider when estimating the test effort. The technique also calculates the standard deviation, which is a measure of the variability or uncertainty of the estimates. The formula for the standard deviation is:
Standard deviation = (most pessimistic - most optimistic) / 6
Using the given values, the standard deviation is:
Standard deviation = (54 - 6) / 6 Standard deviation = 8 person hours
The standard deviation can be used to determine a range of possible values for the test effort, based on a certain level of confidence. For example, using a 68% confidence level, the range is:
Expected value ± standard deviation
Using the calculated values, the range is:
30 ± 8 person hours
Therefore, the final estimate is between 22 person hours and 38 person hours, which is option A.
NEW QUESTION # 54
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