Two Dimensional Multi-Release Software Reliability Modeling and Optimal Release Planning

Long-lived software systems evolve through new product releases, which involve up-gradation of previous released versions of the software in the market. But, upgrades in software lead to an increase in the fault content. Thus, for modeling the reliability growth of software with multiple releases, w...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on reliability 2012-09, Vol.61 (3), p.758-768
Main Authors: Kapur, P. K., Pham, H., Aggarwal, A. G., Kaur, G.
Format: Article
Language:English
Subjects:
Computer programs
Failure
Genetic algorithms
Markets
Mathematical model
Mathematical models
Multi-release planning
Optimization
Planning
Software
Software reliability
software reliability growth model
Studies
Testing
two dimensional software reliability growth model
Upgrading
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Long-lived software systems evolve through new product releases, which involve up-gradation of previous released versions of the software in the market. But, upgrades in software lead to an increase in the fault content. Thus, for modeling the reliability growth of software with multiple releases, we must consider the failures of the upcoming upgraded release, and the failures that were not debugged in the previous release. Based on this idea, this paper proposes a mathematical modeling framework for multiple releases of software products. The proposed model takes into consideration the combined effect of schedule pressure and resource limitations using a Cobb Douglas production function in modeling the failure process using a software reliability growth model. The model developed is validated on a four release failure data set. Another major concern for the software development firms is to plan the release of the upgraded version. When different versions of the software are to be released, then the firm plans the release on the basis of testing progress of the new code, as well as the bugs reported during the operational phase of the previous version. In this paper, we formulate an optimal release planning problem which minimizes the cost of testing of the release that is to be brought into market under the constraint of removing a desired proportion of faults from the current release. The problem is illustrated using a numerical example, and is solved using a genetic algorithm.
ISSN:0018-9529
1558-1721
DOI:10.1109/TR.2012.2207531