CTAL-TTA Exam Dumps : Certified Tester Advanced Level Technical Test Analyst

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​​​​​​.

Analysis:

The significant non-functional defects recorded for the BestForFood system indicate issues that could be mitigated by appropriate non-functional testing strategies:

Portability Testing:

Ensures that the application can operate in different environments without issues. This could have addressed the defect related to the layout and navigation consistency across different websites (Defect A).

Compatibility Testing:

Ensures that the application works well with other systems and environments. This could have helped in preventing the performance issues noticed in the BestForPensions and BestForMortgages websites after the deployment of BestForFood (Defect B).

Explanation of Incorrect Options:

C. The time taken to diagnose, repair and re-test production defects: This is more related to maintainability and operational processes.

D. BestForFood performance impacted by data growth: This is better addressed through performance and scalability testing.

E. Installation time exceeding agreed timescales: This is related to deployment and operational efficiency.

References:

The ISTQB CTAL-TTA syllabus and standard non-functional testing practices emphasize the importance of portability and compatibility testing for ensuring smooth operation across different environments and systems.

Sources:

ISTQB-CTAL-TTA Syllabus

General knowledge on non-functional testing practices.

The key context here is the challenge of managing limited resources, particularly ROM and RAM, due to severe restrictions on code size in consumer electronics. Lazy instantiation is a design pattern that defers the creation of an object until the first time it is needed. This approach can significantly reduce the application's memory footprint by avoiding unnecessary pre-allocation of memory, which is particularly valuable in systems where memory resources are constrained. In reviewing the architecture for such a system, it's crucial to ensure that objects are only created when necessary and that memory is optimally managed. Hence, the focus on lazy instantiation would be most important to ensure that the system uses resources efficiently and remains within the restricted code size.