By Topic

Estimation and Analysis of Some Generalized Multiple Change-Point Software Reliability Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chin-Yu Huang ; Department of Computer Science, National Tsinghua University, Hsinchu, Taiwan ; Michael R. Lyu

Software typically undergoes debugging during both a testing phase before product release, and an operational phase after product release. But it is noted that the fault detection and removal processes during software development and operation are different. For example, the fault removal during operation occurs generally at a slower pace than development. In this paper, we derive a powerful, easily deployable technique for software reliability prediction and assessment in the testing and operational phases. We first review how several existing software reliability growth models (SRGM) based on non- homogeneous Poisson processes (NHPP) can be readily derived from a unified theory. With the unified theory, we further incorporate the concept of multiple change-points, i.e. points in time when the software environment changes, into software reliability modeling. Several models are proposed and discussed under both ideal and imperfect debugging conditions. We estimate the parameters of the proposed models by employing real software failure data, and give a fair comparison with some existing SRGM. Numerical results show that the proposed models can provide good software reliability prediction in the various stages of software development and operation. Our approach is flexible; we can model various environments ranging from exponential-type to S-shaped NHPP models.

Published in:

IEEE Transactions on Reliability  (Volume:60 ,  Issue: 2 )