Software Test-Run Reliability Modeling with Non-homogeneous Binomial Processes

EasyChair Preprint 767

Abstract

While the number of test runs (test cases) is often used to define the time scale to measure quantitative software reliability, the common calendar-time modeling with non-homogeneous Poisson processes (NHPPs) is approximately applied to describe the time scale and the software fault-count phenomena as well. In this paper we give a conjecture that such an approximate treatment is not theoretically justified, and propose a simple test-run reliability modeling framework based on non-homogeneous binomial processes (NHBPs). We show that the Poisson-binomial distribution plays a central role in the software test-run reliability modeling, and apply it to the software release decision. In numerical experiments with seven software fault count data we compare the NHBP based software reliability models (SRMs) with their corresponding NHPP based SRMs and refer to an applicability of NHBP based software test-run reliability modeling.

Keyphrases: Non-homogeneous Poisson process, Poisson Binomial Distribution, goodness-of-fit, non-homogeneous binomial process, prediction, software release decision, software reliability, test-run reliability

@booklet{EasyChair:767,
  author    = {Yunlu Zhao and Tadashi Dohi and Hiroyuki Okamura},
  title     = {Software Test-Run Reliability Modeling with Non-homogeneous Binomial Processes},
  doi       = {10.29007/ng6w},
  howpublished = {EasyChair Preprint 767},
  year      = {EasyChair, 2019}}