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This study introduces a novel software reliability prediction model that improves scalability for large and concurrent systems. The enhanced model reduces computational cost, enabling developers to assess system reliability under various loads.

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architecture-based predictioncomponent-basedreliabilitysensorssoftware designsoftware quality

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Area of Science:

  • Computer Science
  • Software Engineering
  • Reliability Engineering

Background:

  • Software reliability is critical, especially for safety-critical systems, to prevent failures with severe consequences.
  • Existing reliability prediction models face scalability challenges with large systems and struggle to model concurrent applications effectively.
  • Current solutions for scalability often incur high computational costs, limiting their practical application.

Purpose of the Study:

  • To propose a novel software reliability prediction model that addresses scalability and concurrent application modeling.
  • To enhance the efficiency of reliability prediction for complex software systems.
  • To provide developers with tools to determine system reliability under different operational loads.

Main Methods:

  • Introduced a system-level scenario synthesis mechanism to mitigate complexity and enhance scalability.
  • Adapted formal statistical distributions for scenario combination to model concurrent applications.
  • Evaluated the proposed model using sensors-based case studies.

Main Results:

  • Demonstrated significant reduction in computational cost compared to existing models, indicating enhanced scalability.
  • Successfully modeled the behavior of concurrent applications through statistical distribution adaptation.
  • Experimental results confirmed the model's effectiveness in predicting software reliability.

Conclusions:

  • The proposed reliability prediction model offers improved scalability and better handling of concurrent applications.
  • The reduction in computational cost makes the model suitable for large and complex software systems.
  • The model empowers system developers to ascertain system reliability limits under varying loads.