ISTQB-CTFL Reviews Questions

Equivalence partitioning and boundary value analysis are test design techniques that are most appropriate for testing the login page of a web site. The page contains fields for user name and password, which are input values that can be divided into partitions of equivalent data. Equivalence partitioning is a technique that divides the input data and output results of a software component into partitions of equivalent data. Each partition should contain data that is treated in the same way by the component. Equivalence partitioning can be used to reduce the number of test cases by selecting one representative value from each partition. Boundary value analysis is a technique that tests boundary values between partitions of equivalent data. Boundary values are values at the edge of an equivalence partition or at the smallest incremental distance on either side of an edge. Boundary value analysis can be used to detect defects caused by incorrect handling of boundary conditions. For example, for testing the user name field, we can identify two equivalence partitions: valid user name (existing and correct) and invalid user name (non-existing or incorrect). The boundary values for these partitions are the minimum and maximum length of user name allowed by the system.

Decision table testing and state transition testing are not suitable for testing the login page of a web site, as they are more applicable for testing components that have multiple inputs and outputs that depend on logical combinations of conditions or events. Decision table testing is a technique that shows combinations of inputs and/or stimuli (causes) with their associated outputs and/or actions (effects). State transition testing is a technique that models how a system transitions from one state to another depending on events or conditions.

Exploratory testing and statement coverage are not suitable for testing the login page of a web site, as they are more applicable for testing components that require learning, creativity and intuition or structural analysis. Exploratory testing is an approach to testing that emphasizes learning, test design and test execution at the same time. Exploratory testing relies on the tester’s skills, creativity and intuition to explore the software under test and discover defects. Statement coverage is a type of structural testing that measures how many statements in a program have been executed by a test suite. Statement coverage can be used to assess the adequacy or completeness of a test suite.

Decision coverage and fault attack are not suitable for testing the login page of a web site, as they are more applicable for testing components that have complex logic or potential errors. Decision coverage is a type of structural testing that measures how many decision outcomes in a program have been executed by a test suite. Decision coverage can be used to assess the adequacy or completeness of a test suite. Fault attack is a type of functional testing that deliberately introduces faults into a system in order to provoke failures or errors. Verified References: [A Study Guide to the ISTQB® Foundation Level 2018 Syllabus - Springer], Chapter 4, page 34-46; Chapter 5, page 47-48.

State transition testing is a black-box testing technique used to analyze the behavior of a system by examining the transitions between different states in response to events. In state transition testing, a state table or diagram is used to represent the states of a system and the transitions between these states triggered by events.

Option D is correct because in state transition testing, all transitions between states should be explicitly shown in the state table. This includes valid transitions that the system is expected to make under normal operation and, where relevant, invalid transitions that should be tested to ensure the system handles unexpected or erroneous inputs gracefully. The state table provides a comprehensive view of how the system should behave, making it possible to create tests that cover all defined transitions.

A decision table is a technique that shows combinations of inputs and/or stimuli (causes) with their associated outputs and/or actions (effects). A decision table consists of four quadrants: conditions (inputs), actions (outputs), condition entries (values) and action entries (results). The following statements about decision tables are true:

II. Test cases derived from decision tables can be used for component tests. Decision tables can be used to test components that have multiple inputs and outputs that depend on logical combinations of conditions. Decision tables can help cover all possible combinations or scenarios in a systematic way.

III. Several test cases can be selected for each column of the decision table. A column of a decision table represents a unique combination of condition entries and action entries. Several test cases can be selected for each column by varying other input values or expected results that are not part of the decision table. The following statements about decision tables are false:

I. Generally, decision tables are generated for low risk test items. Decision tables are not related to risk level, but rather to complexity level. Decision tables are generated for test items that have complex logic or multiple conditions and actions that need to be tested.

IV. The conditions in the decision table represent negative tests generally. The conditions in the decision table represent both positive and negative tests, depending on whether they are valid or invalid inputs for the test item. Verified References: A Study Guide to the ISTQB® Foundation Level 2018 Syllabus - Springer, Chapter 4, page 42-43.

In Agile teams using the planning poker technique for estimating user stories, it is common practice to have further discussions and rounds of estimation if there is a significant discrepancy in the initial estimates. This helps in reaching a consensus and ensures that all team members understand the complexity and requirements of the user story. According to the ISTQB CTFL syllabus, planning poker involves discussions to clarify differences in estimates, especially when there is a wide range of values selected. By having Memb6 and Memb4, who provided the most pessimistic and optimistic estimates, explain their reasoning, it fosters a deeper understanding and encourages the team to converge towards a more accurate and agreed-upon estimate.

References: ISTQB CTFL Syllabus, Section on Agile methodologies and estimation techniques.