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Mathematical Problems in Engineering
Volume 2015, Article ID 613170, 17 pages
Research Article

The Assessment and Foundation of Bell-Shaped Testability Growth Effort Functions Dependent System Testability Growth Models Based on NHPP

1Department of Automation, Xi’an Institute of High-Tech, Xi’an, Shaanxi 710025, China
2Institute of Construction Engineering Research, General Logistics Department of PLA, Xi’an, Shaanxi 710032, China
3Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology, Changsha, Hunan 410073, China

Received 29 October 2014; Accepted 8 December 2014

Academic Editor: Gang Li

Copyright © 2015 Tian-Mei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper investigates a type of STGM (system testability growth model) based on the nonhomogeneous Poisson process which incorporates TGEF (testability growth effort function). First, we analyze the process of TGT (testability growth test) for equipment, which shows that the TGT can be divided into two committed steps: make the unit under test be in broken condition to identify TDL (testability design limitation) and remove the TDL. We consider that the amount of TGF (testability growth effort) spent on identifying TDL is a crucial issue which decides the shape of testability growth curve and that the TGF increases firstly and then decreases at different rates in the whole life cycle. Furthermore, we incorporate five TGEFs: an Exponential curve, a Rayleigh curve, a logistic curve, a delayed S-shape curve or an inflected S-shaped curve which are collectively referred to as Bell-shaped TGEFs into STGM. Results from applications to a real data set of a stable tracking platform are analyzed and evaluated in testability prediction capability and show that the Bell-shaped function can be expressed as a TGF curve and that the logistic TGEF dependent STGM gives better predictions based on the real data set.