Analysis of incorporating logistic testing-effort function into software reliability modeling

TLDR

Existing SRGMs model testing effort with exponential, Rayleigh, or Weibull curves, which may not fit all development environments. The paper proposes an NHPP‑based SRGM that incorporates a logistic testing‑effort function, demonstrates its predictive accuracy on real failure data, and presents an optimal release policy guided by cost‑reliability criteria. Parameters are estimated from real test/debug data, and experiments on actual datasets evaluate the logistic‑based NHPP SRGM. The logistic‑based SRGM outperforms existing models on real data.

Abstract

This paper investigates a SRGM (software reliability growth model) based on the NHPP (nonhomogeneous Poisson process) which incorporates a logistic testing-effort function. SRGM proposed in the literature consider the amount of testing-effort spent on software testing which can be depicted as an exponential curve, a Rayleigh curve, or a Weibull curve. However, it might not be appropriate to represent the consumption curve for testing-effort by one of those curves in some software development environments. Therefore, this paper shows that a logistic testing-effort function can be expressed as a software-development/test-effort curve and that it gives a good predictive capability based on real failure-data. Parameters are estimated, and experiments performed on actual test/debug data sets. Results from applications to a real data set are analyzed and compared with other existing models to show that the proposed model predicts better. In addition, an optimal software release policy for this model, based on cost-reliability criteria, is proposed.