Functional testing is an example of black-box dynamic testing. Functional testing is the process of testing the functionality or behavior of a software system based on its requirements or specifications. Functional testing does not require any knowledge of the internal structure or implementation of the system under test; it only focuses on what the system does rather than how it does it. Functional testing is also an example of dynamic testing because it involves executing the software system with selected inputs and observing its outputs and effects on the environment. Dynamic testing is different from static testing, which involves examining the software system without executing it, such as code inspection, static analysis, etc. You can find more information about functional testing and dynamic testing in  A Study Guide to the ISTQB® Foundatio n Level 2018 Syllabus , Chapter 2 1 .

Confirmation testing, also known as re-testing, is the process of testing a defect that has been fixed to verify that it has been resolved and does not affect other parts of the system. Confirmation testing is usually done by the same tester who reported the defect and using the same test case that revealed the defect. You can find more information about confirmation testing in  A Study Guide to the I STQB® Foundation Level 2018 Syllabus , Chapter 3, Section 3.2 1 .

Multiple condition coverage is a structure-based test technique that aims to achieve a high level of coverage by testing all possible combinations of conditions and outcomes in each decision point of the code or design of the system under test. Multiple condition coverage verifies that each condition in a decision point has an effect on the outcome and that there are no hidden defects or logical errors in the code or design. Multiple condition coverage requires some knowledge of the internal structure or implementation of the system under test; it focuses on how the system does what it does rather than what it does. Multiple condition coverage is one of the most rigorous and complex structure-based test techniques because it can generate a large number of test cases for each decision point, especially if there are many conditions involved. You can find more information about multiple condition coverage in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 4, Section 4.3.

Regression testing is a type of testing that is performed after a software system has been changed or modified to verify that the changes have not introduced any new defects or adversely affected the existing functionality of the system. Regression testing can be performed when the software system undergoes different types of changes, such as:

Corrective changes: Changes that fix defects or errors in the software system

Adaptive changes: Changes that adapt the software system to new platforms or environments

Perfective changes: Changes that improve the performance or usability of the software system

Preventive changes: Changes that avoid potential problems or issues in the software system

In this case, the software system was re-deployed because the backend database was changed from one vendor to another, which is an example of an adaptive change. Therefore, the test manager decided to perform some functional tests on the re-deployed system to ensure that the change did not affect the functionality of the system. This is an example of regression testing.

The other types of testing mentioned in the question are not relevant for this scenario. For example:

Non-functional testing: This type of testing verifies the non-functional aspects of the software system, such as reliability, performance, security, usability, etc.

Structural testing: This type of testing verifies the internal structure or implementation of the software system, such as code, architecture, design, etc.

Unit testing: This type of testing verifies individual components or units of software in isolation from other components or systems.

You can find more information about regression testing in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 2, Section 2.4.

The requirement given in the image specifies a field asking for the number of group members, which can be anywhere from 2 to 20 visitors. To test this requirement, we can use boundary value analysis, which is a specification-based test technique that involves testing the values at or near the boundaries of an equivalence partition. An equivalence partition is a set of values that are expected to be treated in the same way by the system under test. For example, based on the requirement, we can identify two equivalence partitions for the input group size:

EP1: Group size < 2 (invalid)

EP2: Group size = 2 (valid)

EP3: 2 < Group size < 20 (valid)

EP4: Group size = 20 (valid)

EP5: Group size > 20 (invalid)

The boundary values for each equivalence partition are the values at or near the edges of the partition. For example, the boundary values for EP1 are -1 and 1. The boundary values for EP2 are 1 and 2. The boundary values for EP3 are 2 and 19. And so on.

To test this requirement using boundary value analysis, we need to select one value from each boundary and test it with different combinations of valid and invalid inputs. For example, we can select the following values:

BV1: Group size = -1 (from EP1)

BV2: Group size = 1 (from EP1 and EP2)

BV3: Group size = 2 (from EP2 and EP3)

BV4: Group size = 19 (from EP3 and EP4)

BV5: Group size = 20 (from EP4 and EP5)

BV6: Group size = 21 (from EP5)

We can then create test cases using these values and different combinations of valid and invalid inputs. For example:

TC1: Group size = -1 - > Invalid input

TC2: Group size = 1 - > Invalid input

TC3: Group size = 2 - > Valid input

TC4: Group size = 19 - > Valid input

TC5: Group size = 20 - > Valid input

TC6: Group size = 21 - > Invalid input

Therefore, we need a minimum of  6  valid test cases to achieve  100%  boundary value coverage based on input group size.

You can find more information about boundary value analysis in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 4, Section 4.2.

Testing tools can be used by both developers and testers for different purposes and at different stages of the software development life cycle. For example, developers can use tools such as unit testing frameworks, code coverage tools, debugging tools, static analysis tools, etc., to improve the quality of their code and find defects early. Testers can use tools such as test management tools, test design tools, test execution tools, test data preparation tools, performance testing tools, etc., to support their testing activities and increase their efficiency and effectiveness. The use of testing tools does not necessarily imply test automation, which is the use of software to perform or support test activities that would otherwise require manual intervention. Test automation is a complex and costly process that requires careful planning, design, implementation, maintenance, and evaluation. The use of testing tools also does not change the role of the test team, which is still responsible for defining the test strategy, designing the test cases, analyzing the test results, reporting the defects, etc. You can find more information about testing tools and test automation in  A Study Guide to the ISTQB® Foundation Level 2018 Syllabus , Chapter 6 1 .

Risk-based testing is an approach to testing that prioritizes and focuses on testing activities based on the level of risk associated with each feature or function of the system under test. Risk-based testing aims to optimize the use of time, resources, and techniques for testing by identifying and addressing the most critical and likely sources of failure or harm in the system under test. Some examples of typical risk-based testing activities are:

Brainstorming sessions are held with a wide variety of stakeholders to identify possible failures in the system: This activity is part of the risk identification process, which involves gathering information and opinions from different perspectives and sources to discover potential risks in the system under test.

Tests are prioritized to ensure that those associated with critical parts of the system are executed earlier: This activity is part of the risk analysis and evaluation process, which involves assessing the probability and impact of each risk and ranking them according to their severity and importance.

The focus of testing is shifted to an area in the system where tests find more defects than expected: This activity is part of the risk mitigation and monitoring process, which involves taking actions to reduce or eliminate the risks and tracking their status and progress.

The evaluation of a risk-management tool to decide which tool to use for future projects is not an example of a typical risk-based testing activity because it is not directly related to testing the system under test based on its risks. Rather, it is an example of a tool selection or evaluation activity, which involves comparing and choosing a tool that can support or enhance the testing process based on criteria such as functionality, usability, reliability, compatibility, cost, etc. You can find more information about risk-based testing in [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus], Chapter 3, Section 3.4.

Test metrics are quantitative measures that are used to monitor, control, and improve the test process and its outcomes. Test metrics can be collected at different levels of testing (test case, test suite, test project, etc.) and can be used for different purposes (planning, estimation, execution, evaluation, etc.). Some examples of common test metrics are:

Percentage of work done in test environment creation: This metric indicates how much effort has been spent on setting up and maintaining the test environment, which includes hardware, software, network, data, tools, etc., that are required for conducting the test activities.

Number of test cases run: This metric indicates how many test cases have been executed during a given period or phase of testing.

Deviation from test milestone dates: This metric indicates how much delay or ahead of schedule the test activities are compared to the planned dates.

Defect density: This metric indicates how many defects have been found per unit of size or functionality of the system under test.

Average number of expected defects per requirement is not a common test metric because it is not easy to estimate or measure how many defects are likely to be found for each requirement. Moreover, this metric does not provide useful information for improving the test process or evaluating the test results. You can find more information about test metrics in  A Study Guide to the ISTQB® Foundation Level 2018 Syllabus , Chapter 5, Section 5.4 1 .

Maintenance testing is a type of testing that is done on an existing system after modifications or migration, or to prevent deterioration or obsolescence. Maintenance testing can be triggered by various events or situations, such as:

System migration from one platform to another, which can affect the functionality, performance, or compatibility of the system.

Preparation for an audit of a system, which can require verifying the compliance of the system with standards or regulations.

Modifications to a system, which can introduce new defects or affect existing ones.

Therefore, statements a, c, and d are correct.

References:  Certified Tester Foundation Level Syllabus , Section 5.5

I and III are “testing general principles”. These principles state that exhaustive testing is impossible and testing can show the presence of defects but cannot demonstrate their absence.  These principles reflect the limitations and objectives of testing software systems 4  states these principles as follows:

Exhaustive testing is impossible: Testing everything (all combinations of inputs and preconditions) is not feasible except for trivial cases. Instead of exhaustive testing, risk analysis and priorities should be used to focus testing efforts.

Testing shows the presence of defects: Testing can show that defects are present, but cannot prove that there are no defects. Testing reduces the probability of undiscovered defects remaining in the software but, even if no defects are found, it is not a proof of correctness.

B, C, and D are incorrect answers. II and IV are not “testing general principles”. These statements are false or misleading. The defects found during the pre-release tests or the operational failures are not uniformly distributed across the system’s software modules (II), as they tend to cluster in certain areas or components. Testing is not context-independent (IV), as it depends on the specific objectives, requirements, risks, and characteristics of the system under test.