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Failure Mode and Effect Analysis (FMEA) is a structured approach to identify and address potential failures in a system, product, process, or service. The major factors involved in calculating risk priorities in FMEA are typically the severity of the potential failure, its likelihood of occurrence, and the ability to detect it. These factors are usually combined to form a Risk Priority Number (RPN) for each potential failure mode identified. However, the specific factors mentioned in the options like functionality, reliability, usability, maintainability, efficiency, and portability are quality characteristics that could be considered in an FMEA analysis but are not directly used for calculating risk priorities. Likewise, financial damage, frequency of use, and external visibility might influence the severity or impact of a failure, but they are not standard factors in calculating risk priorities in the context of FMEA. Therefore, the most relevant factors for calculating risk priorities in an FMEA context would typically be the likelihood of the failure occurring and its potential impact, which aligns with option C: Likelihood and impact.

It's important to note that while these explanations are based on general principles and practices related to fault seeding and FMEA, the specifics might vary slightly in different contexts or with different methodologies.

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.

The provided pseudo code for the Answer program shows a WHILE loop that will always execute because the condition for the loop to terminate (a >= d) is never met within the loop's body. This results in an infinite loop. Additionally, since the value of 'b' is initialized with 'a + 10' and 'a' starts from a value that is read and then set to 2, 'b' will never be equal to 12. Therefore, the 'THEN' branch of the IF statement, which includes 'print(b)', is unreachable. These are control flow anomalies because they represent logic in the code that will not function as presumably intended.

Cyclomatic complexity is a measure of the number of linearly-independent paths through a program's source code, which is often used as a measure of the complexity of a program. The control flow graph provided represents the logic of a software component and has more than 5 nodes with decision points, indicating that the complexity would exceed the maximum allowed value of 5. The calculation for cyclomatic complexity is V(G) = E - N + 2P, where E is the number of edges, N is the number of nodes, and P is the number of connected components. In this case, the calculated cyclomatic complexity exceeds the allowed threshold, thus a defect should be reported.

Reliability testing is aimed at verifying the software's ability to function under expected conditions for a specified period of time. It is typically conducted during system testing, where the software is tested in its entirety to ensure that all components work together as expected in an environment that closely simulates the production environment. Reliability testing is not typically associated with static testing, component testing, or functional acceptance testing, as these levels of testing do not address the overall behavior of the system over time.