CTAL-TTA Certified Tester Advanced Level Technical Test Analyst Questions and Answers

The pseudo-code provided for the "Win" program reads in variables A, B, C, and D. However, only variables A, B, and C are used in the conditional statements to determine if the output will be "Win" or "Loose". Variable 'D' is never used after it is read, which is a classic example of a 'defined but not used' data flow anomaly. This means that while there is an instruction to read a value into variable 'D', there is no subsequent use of this variable in the program's logic or output.

Fault seeding is a method used to evaluate the effectiveness of a testing process. Tools designed for fault seeding intentionally insert known defects into the source code, which are then supposed to be discovered during testing. The main purpose is not to check the input checking capabilities, support specification-based test design techniques, or assess maintainability of the software, but rather to gauge how well the testing process can identify and capture defects. By comparing the number of seeded faults that are found against the total number of faults inserted, test teams can get an insight into the effectiveness of their testing strategies and coverage. This method helps in understanding the detection capabilities of testing efforts and in identifying potential areas for improvement in test processes.

To achieve 100% decision coverage with the minimum number of test cases, we need to ensure that every branch of the decision is taken at least once. For the code provided:

• The first condition(a>b)is true for the first two test cases and false for the third.
• The second condition(b>c)is true for the first test case, false for the second, and does not matter for the third since the first condition is false.

Therefore, with these three test cases, we cover all possible outcomes of the decision, ensuring 100% decision coverage.

Performance efficiency testing is most commonly associated with system testing. This is the level at which the complete, integrated system is evaluated, and it is typically where performance, load, and stress testing are conducted to assess the system's behavior under various conditions and loads. Performance efficiency testing at this level helps to ensure that the system meets the necessary performance criteria as a whole.

In the context of an internet banking project where reliability and maintainability are emphasized, a key factor for maintainability is the modularity of the system. Implementing the user interface independently from other software modules (answer B) can significantly enhance maintainability. This is because it allows changes to be made to the user interface withoutimpacting the underlying business logic or data access layers, making the system more adaptable to change. This kind of separation of concerns is a recognized best practice in software design for maintainability. The other options (A, C, and D) relate more to reliability and security aspects than to maintainability.

Performance testing is a key part of ensuring that a system can handle the expected load and perform well under high demand. This type of testing is designed to test the robustness, speed, scalability, and stability of the system under a given workload. It is not primarily concerned with security threats such as code insertion (Option A), nor with fault tolerance in terms of unexpected input values (Option B), nor with the speed of addressing user-reported defects (Option C), although these may be tangential benefits. Performance testing is focused on ensuring that the system meets performance criteria and can handle increasing loads without degradation of service, which is essential for providing a good user experience and for the system’s reliability.

In the context of safety-critical systems, particularly in the medical domain, reliability is of utmost importance. For such systems, it is crucial to ensure that the software can handle unexpected input values and continue to operate without failure. This is essential to ensure patient safety and compliance with FDA requirements. Vulnerability scans (option A) are more related to security testing, whereas scalability (option C) and installation/de-installation (option D) are important but not specifically related to the reliability and safety criticality of the system in the medical domain.

Maintainability metrics typically used with static analysis include measures that reflect the complexity and understandability of the code, such as Number of Lines of Code (LOC), Number of Function Calls, and Comment Frequency. These metrics help in assessing how easily the software can be understood, modified, and maintained. Mean Time Between Failures (MTBF), on the other hand, is a reliability metric. It measures the time elapsed between inherent failures of a system during operation. MTBF is used to predict the system's reliability and is not directly related to the maintainability of the code. Reliability metrics like MTBF would be used in the testing phase to measure the operational reliability of the system rather than during static analysis for maintainability assessment.

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.

An effective review preparation by a Technical Test Analyst includes ensuring that participants are well-prepared and that they spend enough time on preparation, which can be managed by checking the rate (option A). The use of review checklists (option C) and providing review training (option D) are also methods that contribute to more effective review preparation. However, managing the logging rate (option B), or the number of defects logged per minute during the meeting, is not related to the preparation phase but rather to the defect detection and logging phase during the actual review meeting. It is not a preparation activity but a review execution activity.