Goel-Okumoto (GO) Model

Introduction

The Goel Okumoto (GO) Model is a software model that uses probabilities to estimate the size and cost of a software project. It relies on two parameters; size (n) and cost (a).

In this article we will explore the distinctions, between the GO Model and the Basic Execution Time Model (BET) delve into the two parameters of the GO Model and touch upon its limitations.

The Goel Okumoto (GO) Model provides an approach to forecasting software costs and estimating project timelines. By taking into account size and cost estimates software developers and project managers can effectively allocate resources. Make informed decisions about software system design and implementation.

However it’s important to note that the models predictions may not always be completely accurate. This is due to its reliance on data analysis well as an assumption that the relationship between size and cost remains consistent over time.

The Goel Okumoto (GO) Model is a software model that uses probability to estimate the size and cost of a software project. It relies on two parameters; size (n) and cost (a).

The Goel-Okumoto (GO) Model is a software reliability model that was developed by Rajiv Goel and Shih-Lien Okumoto in 1979. It is based on the following assumptions:

• The number of failures experienced by time $t$ follows a Poisson distribution with the mean value function $\mu (t)$.
• The number of software failures that occur in $(t,t+\Delta t]$ with $\Delta t\to 0$ is proportional to the expected number of undetected errors, $N-\mu (t)$. The constant of proportionality is $\varphi$.
• The number of failures occurring in each of the disjoint intervals (0,t1​),(t1​,t2​),…,(tn−1​,tn​) is independent.
• Whenever a failure has occurred, the fault that caused it is removed instantaneously and without introducing any new fault into the software.

The model uses a differential equation to describe the failure intensity, or the rate of occurrence of failures, as a function of time:

dtdμ(t)​=ϕ[N−μ(t)]

where $N$ is the expected total number of faults that exist in the software before testing and $\varphi$ is the failure detection rate or the failure intensity of a fault.

The solution of the differential equation is given by:

μ(t)=N[1−e−ϕt]

This model is known as the exponential NHPP (non-homogeneous Poisson process) model, because it assumes that the failure intensity is not constant, but depends on the number of remaining faults in the software.

The parameters $N$ and $\varphi$ can be estimated using the maximum likelihood estimation (MLE) method, based on the observed failure data. The MLE method involves solving a system of equations that depends on the type of data available. There are two types of data: Type-I data, which consists of the failure times t1​,t2​,…,tn​, and Type-II data, which consists of the number of failures $n$ observed by time $T$.

The Goel-Okumoto model is a simple and popular model that can be used to predict the reliability of software systems, based on the assumption that the failure intensity decreases exponentially with time. However, the model also has some limitations, such as:

• It assumes that all faults have the same failure intensity, which may not be realistic for complex software systems that have different types of faults with different severities and probabilities of occurrence.
• It assumes that the fault removal process is perfect and instantaneous, which may not be true for practical software development environments that have delays, errors, or rework in the fault correction activities.
• It assumes that the software system is tested under constant operational conditions, which may not be valid for software systems that have varying workloads, inputs, or configurations.
  

Some key features of the Goel Okumoto (GO) Model are;

1. Size Parameter (n); The size parameter represents the amount of source code needed for completing the software project. The model calculates this based on data and project attributes.

2. Cost Parameter (a); The cost parameter represents the estimated expenses of the software project, including development, maintenance and other associated costs. The model estimates this by analyzing the relationship, between size and cost using data.

3.Accuracy in Prediction; The Goel Okumoto (GO) Model employs analysis and historical data to predict software costs and sizes. This provides an approach for estimating project timelines and allocating resources.

4.Sensitivity to Size and Cost; The models predictions are affected by changes, in both size and cost parameters, which can vary depending on project requirements technology used and market conditions.
The model makes the assumption that the relationship, between size and cost will stay the same over time. However this may not always be true because of factors like advancements changes in project scope and economic conditions.

Even though it has its limitations the Goel Okumoto (GO) Model provides a tool for software developers and project managers to make informed choices, about software system design and implementation.

conclusion

To summarize the Goel Okumoto (GO) Model serves as a tool, for estimating software project size and cost. It operates based on two parameters; size (n) and cost (a).When software developers and project managers take into account the size and cost estimates of a project it allows them to allocate resources effectively and make well informed decisions, about how to design and implement the software system. However it’s important to note that the models predictions may not always be entirely accurate because they rely on data and statistical analysis. Additionally the assumption that the relationship between size and cost remains consistent, over time may not hold true in all cases.

Frequently Asked Questions about the Goel Okumoto (GO) Model;

1. What is the GO Model, about?

The GO Model is a software model that uses probability to estimate the size and cost of a software project. It relies on two parameters; size (n) and cost (a).

2. How does the GO Model differ from the Basic Execution Time Model (BET)?

While the BET Model focuses on predicting program execution time the GO Model primarily aims to estimate the size and cost of a software project. The GO Model can be utilized during the software system design phase to predict costs while the BET model is more relevant when addressing variability and uncertainty in software system behavior and performance during system operation.

3. What are the two fundamental parameters of the GO Model?

The two fundamental parameters of the GO Model are;
Size (n); This refers to the number of modules or source files, in a software project.
Cost (a); This indicates how many modules are added or modified to fix one defect in a software project.

4. How does the GO Model incorporate variability and uncertainty when estimating software project costs?

The GO Model makes use of the Modified Software Effort Function (MOVE) which was developed by the Software Engineering Institute, at Carnegie Mellon University. This function is based on an analysis of software projects and helps establish a relationship, between project size and cost. By integrating this function into the GO Model it becomes capable of considering the variability and uncertainty that come with estimating software project costs.