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Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
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Risk Prediction by Using Artificial Neural Network in Global Software Development.

Asim Iftikhar^1,2, Muhammad Alam^2,3,4, Rizwan Ahmed¹

¹College of Computer Science and Information Systems, Institute of Business Management (IoBM), Korangi Creek, Karachi, Pakistan.

Computational Intelligence and Neuroscience

|February 24, 2022

Summary

This summary is machine-generated.

Global software development faces risks in time, cost, and resources. Bayesian Regularization neural network models accurately predict these risks for distributed teams, outperforming other methods.

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Area of Science:

Computer Science
Software Engineering
Risk Management

Background:

The global software development landscape is expanding due to the dispersed nature of software expertise worldwide.
Integrating globally distributed skills and tools presents significant challenges in managing software projects.
Risk management strategies are crucial for addressing the complexities faced by software experts in a competitive environment.

Purpose of the Study:

To predict risks associated with time, cost, and resources in global software development projects.
To evaluate the effectiveness of different neural network approaches in risk prediction for distributed teams.
To identify the most accurate algorithm for predicting project risks in a global context.

Main Methods:

Implementation of three neural network approaches: Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient.
Application of these models to predict risks related to project time, cost, and resources.
Comparative analysis of the algorithms' performance based on accuracy metrics, specifically Mean Squared Error (MSE).

Main Results:

Bayesian Regularization demonstrated superior performance in predicting risks compared to Levenberg-Marquardt and Scaled Conjugate Gradient.
The study identified Bayesian Regularization as the most accurate algorithm for risk prediction in global software development based on MSE (validation).
The findings highlight the potential of specific neural network models in mitigating project uncertainties.

Conclusions:

Neural network approaches, particularly Bayesian Regularization, offer effective solutions for predicting and managing risks in global software development.
Accurate risk prediction is vital for the successful completion of projects involving distributed teams.
This research provides valuable insights for optimizing risk management strategies in the evolving field of global software engineering.