CTAL-TTA Exam Dumps - ISTQB Advance Level Questions and Answers

Increasing automated test coverage frees up valuable time for testers, allowing them to focus more on exploratory testing, particularly in high-risk areas. This approach leverages the efficiency of automation to handle routine, repeatable testing tasks, while human testers can focus their expertise on more complex, risk-prone areas that require creative and critical thinking .

The epic described has broad and overarching objectives that span multiple user groups and stakeholders, making it inherently dependent on other functionalities within the same system. This fails the 'Independent' criterion of INVEST, which suggests features should be self-contained. Moreover, the epic's scope suggests it's quite large, likely requiring splitting into smaller, more manageable stories, thus not meeting the 'Small' criterion. INVEST aims for manageable, independent increments that can be developed and validated quickly .

To achieve 100% decision coverage, we need to ensure that every decision (if statement) in the code has been tested for both true and false outcomes. Let's analyze the code and count the decisions:

IF (Room_price > 200)

IF (Loyalty_level = Platinum) inside Room_price > 200

ELSE within the same decision above

IF (Loyalty_level = Gold) inside the ELSE of decision 2

IF (Room_price > 150)

IF (Loyalty_level = Platinum) inside Room_price > 150

ELSE within the same decision above

IF (Discount_factor < 100%)

To cover all these decisions, we need test cases for each possible combination:

Room_price > 200 and Loyalty_level = Platinum.

Room_price > 200 and Loyalty_level = Gold.

Room_price > 200 and Loyalty_level not Platinum or Gold.

Room_price <= 200 and Room_price > 150, and Loyalty_level = Platinum.

Room_price <= 200 and Room_price > 150, and Loyalty_level not Platinum.

Room_price <= 150.

From these scenarios, we can see that the minimum number of test cases required to achieve 100% decision coverage is four:

Test with Room_price > 200 and Loyalty_level = Platinum (tests decision 1 true, 2 true, and 8 true).

Test with Room_price > 200 and Loyalty_level = Gold (tests decision 1 true, 2 false, 4 true, and 8 true).

Test with Room_price > 200 and Loyalty_level not Platinum or Gold (tests decision 1 true, 2 false, 4 false, and 8 true).

Test with Room_price <= 200 and Room_price > 150, and Loyalty_level = Platinum (tests decision 1 false, 5 true, 6 true, and 8 true).

Test with Room_price <= 200 and Room_price > 150, and Loyalty_level not Platinum (tests decision 1 false, 5 true, 6 false, and 8 true).

Test with Room_price <= 150 (tests decision 1 false, 5 false, and 8 false).

Given that these six cases test all branches, four test cases are required to ensure that all decisions are covered at least once.

Therefore, the correct answer is A. 4.

To achieve 100% statement coverage, you need to ensure every executable statement in the code is executed at least once during testing. Given the code:

If x > y and z = 3 statement1 elseif z = 4 statement2 endif;

Two tests are required:

Test 1: Set x > y and z = 3 to execute statement1.

Test 2: Set z = 4 (irrespective of the condition x > y) to execute statement2.

With these two tests, both conditions that lead to the execution of statement1 and statement2 are covered.

In a continuous integration environment, it's true that automation tools are used to verify the results of the build. This practice ensures that as new code integrations occur, they do not disrupt existing functionality and that all tests pass successfully before further deployments.

Explanation: Continuous integration environments typically involve automated builds and testing to quickly identify integration issues and ensure that software remains in a state where it can be reliably released at any time.

The statement about performance testing tools that is NOT correct is that they typically drive the application by simulating user interaction at the graphical user interface (GUI) level to more accurately measure response times. In practice, performance testing tools often simulate user interactions at a protocol or service level rather than the GUI level. This approach allows the tools to generate a high load by simulating many virtual users, which would be challenging to achieve with GUI-level interactions due to the higher resource consumption and slower execution speed associated with GUI automation.

Performance testing tools are designed to assess the performance of a system under a particular load and are not primarily focused on the user interface. They simulate multiple users accessing the system simultaneously, which helps in identifying bottlenecks, understanding the system's behavior under load, and determining how the system scales with increasing load. The tools typically simulate user requests to the server, bypassing the GUI to directly test the backend, APIs, or other service endpoints. This method allows for more efficient and scalable testing, enabling the simulation of thousands of users without the overhead of rendering the GUI.

To achieve decision coverage, each branch of the decision structure in the code must be executed at least once. The code snippet has three branches:

withdrawal-amount <= amount-on-deposit

withdrawal-amount <= $100 AND preferred-customer = true

Default case where the transaction is not authorized

Data set #1 triggers the second branch because the withdrawal amount is not less than the amount on deposit, but it is less than $100, and the user is a preferred customer.

Data set #2 triggers the first branch where the withdrawal amount is equal to the amount on deposit.

Data set #3 tests the condition where the withdrawal amount is less than the deposit amount, but the user is not a preferred customer, triggering the default case.

All decision branches are covered by these test cases, achieving 100% decision coverage.

Common issues with traditional capture/playback test automation include difficulty in maintaining the scripts when software changes (option A), the challenge for non-technical persons to maintain recorded scripts (option B), and the issue that data and actions are often intertwined within the recorded script (option C), which can make them hard to understand and modify. However, the timing of the execution of the recorded script (option D), such as the difficulty of running scripts outside office hours, is not typically a problem inherent to capture/playback test automation itself but rather an environmental or scheduling issue.

Given the absence of defined performance requirements for the new system, a practical approach is to use the performance metrics of the legacy system as a benchmark. This method is beneficial as it provides a clear, historical baseline of what users are accustomed to and accept as satisfactory performance. Benchmarking against the legacy system ensures the new system meets or exceeds the performance levels that users already find acceptable, which can facilitate smoother acceptance and transition to the new system​​.

A common technical issue causing automation projects to fail in delivering the planned return on investment is the lack of maintainability in their design. Automation frameworks and scripts that are not designed with maintainability in mind can become cumbersome to update and scale as the software evolves. This results in increased costs and effort over time to keep the automation relevant and effective, which can erode the expected returns on investment from the automation initiative .