Predictive Modeling in Practice: A Case Study with AgenaRisk

Written by bayesianinference | Published 2025/08/27
Tech Story Tags: bayesian-networks | extreme-programming | software-process | xp-process-modelling | financial-institutions | model-validation | software-engineering | data-science

TLDRLearn about the implementation and validation of a Bayesian Network model for Extreme Programming (XP) using the AgenaRisk toolset. via the TL;DR App

Table of Links

Abstract and 1. Introduction

  1. Background and 2.1. Related Work

    2.2. The Impact of XP Practices on Software Productivity and Quality

    2.3. Bayesian Network Modelling

  2. Model Design

    3.1. Model Overview

    3.2. Team Velocity Model

    3.3. Defected Story Points Model

  3. Model Validation

    4.1. Experiments Setup

    4.2. Results and Discussion

  4. Conclusions and References

4. MODEL VALIDATION

The proposed model was implemented using AgenaRisk toolset [1]. AgenaRisk is a powerful tool for modelling risk and making predictions based on Bayesian Network. AgenaRisk has the following features:

  • It integrates the advantages of Bayesian Networks, statistical simulation and spreadsheet analysis.

  • A wide range of built-in conditional probability functions are available.

  • It has the ability to build dynamic models.

  • AgenaRisk is visual, simple and powerful tool.

A free licence for AgenaRisk toolset is available through the company website (http://www.agenarisk.com), but limited to 7 days.

In the next section, experiments setup will be illustrated, while the results will be provided and discussed in the following section

Authors:

(1) Mohamed Abouelelam, Software System Engineering, University of Regina, Regina, Canada;

(2) Luigi Benedicenti, Software System Engineering, University of Regina, Regina, Canada.


This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.


Written by bayesianinference | At BayesianInference.Tech, as more evidence becomes available, we make predictions and refine beliefs.
Published by HackerNoon on 2025/08/27