Question: More than ( 4 0 - 6 0 % ) of tasks in software engineering are related to code reviews. The following

More than \(40-60\%\) of tasks in software engineering are related to code reviews. The following Python code is written to calculate the software reliability and compute the Risk Priority Number for different failure modes. Affer reviewing it, which software reliability model has been used?
A) Fault Tree Analysis (FTA)
B) Failure Mode and Effects Analysis (FMEA)
C) Markov Model
D) Weibull Analysis
\# Define the failure modes with their severity, occurrence, and detection values
failure modes \(=\{\)
"Mode 1": \{"severity": 8, "occurrence": 5, "detection": 2\},
"Mode 2": \{"severity": 6, "occurrence": 3, "detection": 3\},
"Mode 3": \{"severity": 10, "occurrence": 2, "detection": 1\},
)
\# Function to calculate Risk Priority Number (RPN)
def calculate mpn(severity, occurrence, detection):
return severity " occurrence * detection
\# Calculate and display the RPN for each failure mode
for mode, values in failure_modes items 0 :
[men = calculate.rmn(values["severity"], values["occurrence"], values["detection"])
print(\(\mathrm{f}^{\prime}\)(mode) RPN: (mal)")
\# Discussion of implications
print("nImplications for Secure Software Engineering (SSE):")
print("1. RPN values help prioritize failure modes based on risk exposure.")
print("2. High RPN values indicate areas that require immediate attention and mitigation strategies.")
print("3. Understanding these risks supports better decision-making regarding resource allocation.")
print("4. In Secure Software Engineering, addressing potential failure modes enhances the reliability of critical systems.")
print("5. Continuous monitoring and analysis can further reduce risks and improve system resilience.")
More than \ ( 4 0 - 6 0 \ % \ ) of tasks in

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