Software Reliability

 Return Home Page

Software Reliability Growth Models -
1) DfRSoft has multiple methods for assessing software reliability growth in testing. These can be viewed in two categories, 1) the well known Goel-Okumoto (G-O) Model, 2)Cumulative Reliability Growth Models. Each is described below:

Goel-Okumoto (G-O) Software Reliability Growth Model
Often it is observed that testing (and hence defect fixing) cycle gets elongated during product development. Manager is asked questions like, “How long will testing go on? How many defects are still remaining in software? How do we predict reliability of current software? At such time, it is better to use statistical methods to take informed decisions, rather than guessing with past experience. The method described here is based on Non-Homogeneous Poisson Process (NHPP) G-O model. It uses historical sample test data to predict how many residual defects are there in the software system and how many days are needed.

Software Reliability Growth Model - DfRSoft uses the well known Goel-Okumoto (G-O) Model

Cumulative Software Reliability Growth Models
DfRSoft offers a number of cumulative software reliability growth models. These model are based on Cumulative Time vs. Cumulative Error Data Fits. They include The Duane Model, Crow/AMSAA Model, The Logarithmic Model, and a Polynomial Fit Model. Often the Logarithmic model is the best chose. However, one should use the model that best fits the data points and projected results. The key to remember is that all the models are based on cumulative time rather than test time. Therefore one should always keep this in mind in projecting model results. Duane and Crow/AMSAAA are well known traditional reliability growth models. They can also be used in software reliability growth. All these models have advantages over the G-O model above. The max errors in the G-O model is the value A. This limits the use of the model. These models do not have this limit. They are more straight forward, being basic regression and well understood. All models are based on data fits of Cum Errors plotted against cumulative time. For the Duane and Crow/AMSAA, the result is a power law fit to the data. Beta and alpha=1-Beta are considered the growth exponent for these models.
Duane Logarithmic Software Reliability Model

When is enough, enough for software reliability testing: DfRSoft now offers a method of slopes test criteria to help the user in assessing this.