ISTQB CTAL-TTA Exam With Confidence Using Practice Dumps

In the data flow analysis of the pseudocode, the variable output presents a potential anomaly. Initially, output is set to input1, but it may be overwritten if input2 is greater. The anomaly, specifically a 'definition-use' (du-path) issue, arises because there might be scenarios where output does not adequately reflect changes depending on the dynamic data flow influenced by the conditional. Proper testing and validation of such conditional structures are crucial to ensure that output consistently represents the largest of the two inputs as intended.

Fault seeding and fault injection tools are used intentionally to introduce defects into a system or software to test the robustness of the test suite. This method helps in validating the effectiveness of the test processes and configurations by ensuring that the tests are capable of detecting and isolating inserted faults. It is a technique used to measure the quality of testing rather than debugging or performance compliance, which relates directly to the fundamental purpose of enhancing test coverage and the detection capability of the test suite .

To determine the percentage of decision testing coverage achieved by the given suite of test cases, we need to analyze the decisions and outcomes in the provided logic.

Detailed Analysis

Identify all decisions in the logic:

IF account number is valid

IF customer account is active

IF oldBalance < zero

IF balance now >= zero

IF account holder is VIP customer

Calculate the number of unique decisions:

There are 5 unique decision points in the logic.

Determine the possible outcomes for each decision:

IF account number is valid → 2 outcomes (True, False)

IF customer account is active → 2 outcomes (True, False)

IF oldBalance < zero → 2 outcomes (True, False)

IF balance now >= zero → 2 outcomes (True, False)

IF account holder is VIP customer → 2 outcomes (True, False)

Test Cases Analysis

Test Case A

Account number valid → True

Account is active → True

OldBalance = -100 → True (less than zero)

Balance now = +200 → True (greater than zero)

Not a VIP customer → False

Test Case B

Account number valid → True

Account is active → True

OldBalance = -100 → True (less than zero)

Balance now = -50 → False (less than zero)

Is a VIP customer → True

Coverage Calculation

To calculate the decision coverage, we need to see how many of the possible outcomes are covered by the test cases:

IF account number is valid (True in both A and B, False not tested)

IF customer account is active (True in both A and B, False not tested)

IF oldBalance < zero (True in both A and B, False not tested)

IF balance now >= zero (True in A, False in B, both outcomes tested)

IF account holder is VIP customer (False in A, True in B, both outcomes tested)

Out of the 10 possible outcomes (2 for each decision), the given test cases cover 6 outcomes:

Account number valid → True

Account number valid → False (not covered)

Customer account active → True

Customer account active → False (not covered)

OldBalance < zero → True

OldBalance < zero → False (not covered)

Balance now >= zero → True

Balance now >= zero → False

Account holder is VIP → True

Account holder is VIP → False

Thus, 6 out of the 10 outcomes are covered by the test cases, giving us a decision coverage percentage of:

Decision Coverage=(610)×100%=60%\text{Decision Coverage} = \left(\frac{6}{10}\right) \times 100\% = 60\%Decision Coverage=(106​)×100%=60%

References:

The sample questions and answers provided in the ISTQB CTAL-TTA documentation confirm this calculation method and the expected coverage percentage for similar logical scenarios​​​​​​.