ISTQB-CTFL Reviews Questions

Decision tables are used to model complex decision logic by considering different combinations of conditions and actions. Based on the given decision table for prescribing drug therapy:

Column 1 and Column 3 both result in the same actions (prescribing Amoxicillin).

These columns can be merged because the actions taken do not depend on whether the patient is taking anticoagulant therapy (both are 'T' for this condition).

Thus, combining these columns simplifies the decision table without losing any information.

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.

Checklist-based testing involves using checklists that contain items, such as potential test conditions, that should be tested. These checklists are often based on insights into what is important to the user, potential areas where software might fail, and specific aspects that need to be tested. It provides a structured yet flexible approach to testing, ensuring key areas are covered while allowing testers to use their experience and understanding of the system. Checklist-based testing is not limited to non-functional testing but can be applied to various types of testing, including functional testing.References:

ISTQB Certified Tester Foundation Level Syllabus v4.0, Section 4.4.5.

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 atest 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 oftransitions 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 outputranges 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.