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A testing-coverage software reliability model considering fault removal efficiency and error generation.

Qiuying Li1,2, Hoang Pham3

  • 1School of Reliability & Systems Engineering, Beihang University, Beijing, China.

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This study introduces an improved software reliability model that accounts for imperfect fault removal and testing coverage. The new model demonstrates superior fitting and predictive performance for software reliability growth.

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Area of Science:

  • Software Engineering
  • Computer Science
  • Reliability Engineering

Background:

  • Traditional software reliability growth models (SRGMs) often assume perfect fault removal.
  • Existing models based on nonhomogeneous Poisson process (NHPP) seldom differentiate between fault detection and removal efficiency.
  • The imperfect debugging phenomenon, where faults may persist or new ones introduced, is common in practice.

Purpose of the Study:

  • To develop a novel NHPP-based software reliability model.
  • To incorporate fault introduction rate, fault removal efficiency, and testing coverage into reliability evaluation.
  • To address the limitations of existing SRGMs in handling imperfect fault removal.

Main Methods:

  • Developed a new SRGM incorporating fault introduction rate, fault removal efficiency, and testing coverage.
  • Utilized testing coverage to represent fault detection rate.
  • Employed fault removal efficiency to model imperfect fault repair.
  • Compared the proposed model against existing NHPP SRGMs using real failure data.

Main Results:

  • The proposed model demonstrated better fitting performance compared to existing NHPP SRGMs.
  • The model exhibited superior predictive performance on three real-world failure datasets.
  • The results validate the importance of considering imperfect fault removal and testing coverage.

Conclusions:

  • The developed model provides a more realistic approach to software reliability evaluation.
  • Incorporating fault removal efficiency and testing coverage significantly enhances model accuracy.
  • The findings suggest practical implications for improving software testing and development processes.