CTFL4 Exam Dumps - ISTQB Certified Tester Foundation Level CTFL 4.0 Exam

For boundary value analysis, the test cases should include the boundary values just inside and just outside the equivalence partition for the 13% interest rate range:

R100,000 (just inside the previous range)

R100,001 (start of the 13% range)

R500,000 (end of the 13% range)

R500,001 (just outside the range)

These values ensure that both the edges of the partition are tested​​.

Metrics can be collected during and at the end of testing activities to assess various aspects including progress against the planned schedule and budget, the current quality of the test object, the adequacy of the test approach, and the effectiveness of the test activities with respect to the objectives. Collecting these metrics helps in understanding the overall performance and quality of the testing process​​.

The correct outcome of a two-value boundary value analysis applied to the password length is the set of test cases represented by option D. Boundary value analysis is a test design technique that focuses on the values at the boundaries of an equivalence partition, such as the minimum and maximum values, or the values just above and below the boundaries. A two-value boundary value analysis uses two values for each boundary, one representing the valid value and one representing the invalid value. For example, if the valid range of values is from 4 to 7, then the two values for the lower boundary are 3 and 4, and the two values for the upper boundary are 7 and 8. The test cases in option D use these values for the password length, while also satisfying the other requirements of the password, such as containing at least one numeric character, one capital letter, and one lowercase letter. The test cases in option D are:

1RhT: a 4-character password that is valid

rSp53: a 5-character password that is valid

3N3e10: a 6-character password that is valid

8sBdby: an 8-character password that is invalid The test cases in the other options are incorrect, because they either use values that are not at the boundaries of the password length, or they do not meet the other requirements of the password. For example, the test cases in option A are:

1xA: a 3-character password that is invalid, but it does not contain a capital letter

aB11: a 4-character password that is valid

Pq1ZZab: a 7-character password that is valid

7iDD0a1x: an 8-character password that is invalid References: ISTQB Certified Tester Foundation Level (CTFL) v4.0 sources and documents:

ISTQB® Certified Tester Foundation Level Syllabus v4.0, Chapter 2.2.1, Black-box Test Design Techniques1

ISTQB® Glossary of Testing Terms v4.0, Boundary Value Analysis, Equivalence Partition2

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.

Option D correctly explains what is represented by the lines A, B and the axes X, Y in a testing metrics chart. According to option D:

X-axis represents Time

Y-axis represents Count

Line A represents Number of open bugs

Line B represents Total number of executed tests

This information is essential in understanding and analyzing the testing metrics of a completed project.

References: ISTQB Certified Tester Foundation Level (CTFL) v4.0 Syllabus, Section 2.5.1, Page 35.

Statement coverage is a structural coverage metric that measures the percentage of executable statements in the source code that are executed by a test suite1. Decision coverage is another structural coverage metric that measures the percentage of decision outcomes (such as branches or conditions) in the source code that are executed by a test suite1. Decision coverage is a stronger metric than statement coverage, because it requires that every possible outcome of each decision is tested, while statement coverage only requires that every statement is executed at least once2. Therefore, if a test suite achieves 100% decision coverage, it also implies that it achieves 100% statement coverage, because every statement in every branch or condition must have been executed. However, the converse is not true: 100% statement coverage does not guarantee 100% decision coverage, because some branches or conditions may have multiple outcomes that are not tested by the test suite2. For example, consider the following pseudocode:

if (x > 0) then print(“Positive”) else print(“Non-positive”) end if

A test suite that executes this code with x = 1 and x = -1 will achieve 100% statement coverage, because both print statements are executed. However, it will not achieve 100% decision coverage, because the condition x > 0 has only been tested with two outcomes: true and false. The third possible outcome, x = 0, has not been tested by the test suite. Therefore, the test suite may miss a potential bug or error in the condition or the branch.

The other options, such as stale transition coverage, equivalence class coverage, and boundary value coverage, are not guaranteed to be 100% by achieving 100% decision coverage. Stale transition coverage is a structural coverage metric that measures the percentage of transitions between states in a state machine that are executed by a test suite3. Equivalence class coverage is a functional coverage metric that measures the percentage of equivalence classes (or partitions) of input or output values that are tested by a test suite4. Boundary value coverage is another functional coverage metric that measures the percentage of boundary values (or extreme values) of input or output ranges that are tested by a test suite4. These metrics are independent of decision coverage, because they are based on different aspects of the system under test, such as its behavior, functionality, or specification. Therefore, achieving 100% decision coverage does not imply achieving 100% of any of these metrics, and vice versa. References = ISTQB® Certified Tester Foundation Level Syllabus v4.0, Test Coverage in Software Testing - Guru99, Structural Coverage Metrics - MATLAB & Simulink - MathWorks India, Test Design Coverage in Software Testing - GeeksforGeeks.

A static analysis tool is best suited for determining source code compliance with coding standards. These tools analyze the code without executing it and can check for adherence to coding standards, syntax errors, and other static properties of the code. The ISTQB CTFL syllabus emphasizes the role of static analysis tools in verifying that code meets predefined standards and guidelines.

References:ISTQB CTFL Syllabus, Section on static testing and tools.

 The V-model is a software development life cycle model that defines four test levels that correspond to four development phases: component (unit) testing with component design, integration testing with architectural design, system testing with system requirements, and acceptance testing with user requirements. The V-model emphasizes the importance of verifying and validating each phase of development with a corresponding level of testing, and ensuring that the test objectives, test basis, and test artifacts are aligned and consistent across the test levels. Therefore, an organization that wants to follow the V-model cannot do away with integration testing, as it would break the symmetry and completeness of the V-model, and compromise the quality and reliability of the software or system under test. Integration testing is a test level that aims to test the interactions and interfaces between components or subsystems, and to detect any defects or inconsistencies that may arise from the integration of different parts of the software or system. Integration testing is essential for ensuring the functionality, performance, and compatibility of the software or system as a whole, and for identifying and resolving any integration issues early in the development process. Skipping integration testing would increase the risk of finding serious defects later in the test process, or worse, in the production environment, which would be more costly and difficult to fix, and could damage the reputation and credibility of the organization. Therefore, the correct answer is D.

The other options are incorrect because:

A. It is not allowed as organizations can decide on the test levels to do depending on the context of the system under test. While it is true that the choice and scope of test levels may vary depending on the context of the system under test, such as the size, complexity, criticality, and risk level of the system, the organization cannot simply ignore or skip a test level that is defined and required by the chosen software development life cycle model. The organization must follow the principles and guidelines of the software development life cycle model, and ensure that the test levels are consistent and coherent with the development phases. If the organization wants to have more flexibility and adaptability in choosing the test levels, it should consider using a different software development life cycle model, such as an agile or iterative model, that allows for more dynamic and incremental testing approaches.

B. It is not allowed because integration testing is not an important test level and can be dispensed with. This statement is false and misleading, as integration testing is a very important test level that cannot be dispensed with. Integration testing is vital for testing the interactions and interfaces between components or subsystems, and for ensuring the functionality, performance, and compatibility of the software or system as a whole. Integration testing can reveal defects or inconsistencies that may not be detected by component (unit) testing alone, such as interface errors, data flow errors, integration logic errors, or performance degradation. Integration testing can also help to verify and validate the architectural design and the integration strategy of the software or system, and to ensure that the software or system meets the specified and expected quality attributes, such as reliability, usability, security, and maintainability. Integration testing can also provide feedback and confidence to the developers and stakeholders about the progress and quality of the software or system development. Therefore, integration testing is a crucial and indispensable test level that should not be skipped or omitted.

C. It is not allowed because integration testing is a very important test level and ignoring it means definite poor product quality. This statement is partially true, as integration testing is a very important test level that should not be ignored, and skipping it could result in poor product quality. However, this statement is too strong and absolute, as it implies that integration testing is the only factor that determines the product quality, and that ignoring it would guarantee a poor product quality. This is not necessarily the case, as there may be other factors that affect the product quality, such as the quality of the requirements, design, code, and other test levels, the effectiveness and efficiency of the test techniques and tools, the competence and experience of the developers and testers, the availability and adequacy of the resources and environment, the management and communication of the project, and the expectations and satisfaction of the customers and users. Therefore, while integration testing is a very important test level that should not be skipped, it is not the only test level that matters, and skipping it does not necessarily mean definite poor product quality, but rather a higher risk and likelihood of poor product quality.

References = ISTQB Certified Tester Foundation Level Syllabus, Version 4.0, 2018, Section 2.3, pages 16-18; ISTQB Glossary of Testing Terms, Version 4.0, 2018, pages 38-39; ISTQB CTFL 4.0 - Sample Exam - Answers, Version 1.1, 2023, Question 104, page 36.