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PERMMA: Enhancing parameter estimation of software reliability growth models: A comparative analysis of metaheuristic
Vishal Pradhan1, Arijit Patra1, Ankush Jain2
1School of Applied Sciences, Kalinga Institute of Industrial Technology, Odisha, India.
Plos One
|September 4, 2024
Summary
This study explores metaheuristic optimization for software reliability growth models (SRGMs). The Regenerative Genetic Algorithm (RGA) and Grey-Wolf Optimizer (GWO) show superior parameter estimation capabilities for SRGMs.
Area of Science:
- Software Engineering
- Reliability Engineering
- Optimization Algorithms
Background:
- Software reliability growth models (SRGMs) are crucial for assessing software dependability.
- Traditional parameter estimation methods like Maximum Likelihood Estimation (MLE) and Least Squares Estimation (LSE) have limitations.
- Metaheuristic optimization algorithms offer advanced solutions for overcoming these limitations in parameter estimation.
Purpose of the Study:
- To analyze the applicability of metaheuristic algorithms for parameter estimation in SRGMs.
- To compare the performance of four metaheuristic algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA).
- To evaluate these algorithms on actual software failure data using established SRGMs.
Main Methods:
- Four metaheuristic algorithms (GWO, RGA, SCA, GSA) were employed for parameter estimation.
- Comparative analysis was conducted using four popular SRGMs and three real-world failure datasets.
- Performance was evaluated based on convergence criteria and R2 distribution.
Main Results:
- Metaheuristic algorithms produced parameter estimates close to LSE values.
- RGA and GWO demonstrated superior performance across various real-world failure datasets.
- RGA showed faster convergence and higher accuracy in locating optimal solutions compared to GWO and other methods.
Conclusions:
- RGA and GWO are highly suitable for parameter estimation in SRGMs.
- The Regenerative Genetic Algorithm (RGA) is recommended for its efficiency and accuracy in optimizing SRGM parameters.
- Metaheuristic approaches provide a robust alternative for enhancing software reliability analysis.
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