Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background and 2.1. Related Work
2.2. The Impact of XP Practices on Software Productivity and Quality
2.3. Bayesian Network Modelling


Model Design
3.1. Model Overview
3.2. Team Velocity Model
3.3. Defected Story Points Model


Model Validation
4.1. Experiments Setup
4.2. Results and Discussion


Conclusions and References Background and 2.1. Related Work
2.2. The Impact of XP Practices on Software Productivity and Quality
2.3. Bayesian Network Modelling Background and 2.1. Related Work Background and 2.1. Related Work 2.2. The Impact of XP Practices on Software Productivity and Quality 2.2. The Impact of XP Practices on Software Productivity and Quality 2.3. Bayesian Network Modelling 2.3. Bayesian Network Modelling Model Design
3.1. Model Overview
3.2. Team Velocity Model
3.3. Defected Story Points Model Model Design Model Design 3.1. Model Overview 3.1. Model Overview 3.2. Team Velocity Model 3.2. Team Velocity Model 3.3. Defected Story Points Model 3.3. Defected Story Points Model Model Validation
4.1. Experiments Setup
4.2. Results and Discussion Model Validation Model Validation 4.1. Experiments Setup 4.1. Experiments Setup 4.2. Results and Discussion 4.2. Results and Discussion Conclusions and References Conclusions and References Conclusions and References ABSTRACT A Bayesian Network based mathematical model has been used for modelling Extreme Programming software development process. The model is capable of predicting the expected finish time and the expected defect rate for each XP release. Therefore, it can be used to determine the success/failure of any XP Project. The model takes into account the effect of three XP practices, namely: Pair Programming, Test Driven Development and Onsite Customer practices. The model’s predictions were validated against two case studies. Results show the precision of our model especially in predicting the project finish time. A Bayesian Network based mathematical model has been used for modelling Extreme Programming software development process. The model is capable of predicting the expected finish time and the expected defect rate for each XP release. Therefore, it can be used to determine the success/failure of any XP Project. The model takes into account the effect of three XP practices, namely: Pair Programming, Test Driven Development and Onsite Customer practices. The model’s predictions were validated against two case studies. Results show the precision of our model especially in predicting the project finish time. 1. INTRODUCTION Extreme Programming (XP) is a lightweight software development methodology. XP is one of the iterative informal development methodologies known as Agile methods. XP comprises a number of values, practices and principles. There is no large requirements and design documents. XP uses what is called User Stories instead of requirements. The XP project comprises of a number of User Stories. Each user stories contains a number of Story Points. The development process constructed from iterative small releases. In each release, User Stories are selected to be developed in this release according to their importance. Managers of XP projects suffer from lack of prediction systems capable of estimating the expected effort and quality of the software development process. Managers need to know the probability of success or failure of XP project. Models capable of predicting the project finish time are very helpful to the project managers. Those models should also be capable of predicting the product quality in terms of the expected number of defects. These requirements should be covered in strong mathematical model. In this paper, a Bayesian Network based mathematical model for XP process is presented. The proposed model satisfies the following features: It considers the iterative nature of XP by modelling the project as a number of sequential releases.


The model able to predict the expected finish time, and therefore it could determine the success/failure of the project.


The prediction can be done in the project planning phase before starting the actual development using very simple input data.


The model tracks the developer velocity (measured in number of Story Points per day) as function of the developer experience. It also models the increase in the developer velocity as the project goes on. It considers the iterative nature of XP by modelling the project as a number of sequential releases. It considers the iterative nature of XP by modelling the project as a number of sequential releases. The model able to predict the expected finish time, and therefore it could determine the success/failure of the project. The model able to predict the expected finish time, and therefore it could determine the success/failure of the project. The prediction can be done in the project planning phase before starting the actual development using very simple input data. The prediction can be done in the project planning phase before starting the actual development using very simple input data. The model tracks the developer velocity (measured in number of Story Points per day) as function of the developer experience. It also models the increase in the developer velocity as the project goes on. The model tracks the developer velocity (measured in number of Story Points per day) as function of the developer experience. It also models the increase in the developer velocity as the project goes on. The model considers the effect of the Pair Programming and Test Driven Development practices on the Team velocity.


The model predicts the process quality by measuring the defect rate in each release.


It considers the effect of the Onsite Customer and Test Driven Development practices on the defect rate. The model considers the effect of the Pair Programming and Test Driven Development practices on the Team velocity. The model considers the effect of the Pair Programming and Test Driven Development practices on the Team velocity. The model predicts the process quality by measuring the defect rate in each release. The model predicts the process quality by measuring the defect rate in each release. It considers the effect of the Onsite Customer and Test Driven Development practices on the defect rate. It considers the effect of the Onsite Customer and Test Driven Development practices on the defect rate. The proposed model was implemented using AgenaRisk toolset [1]; a toolset for modelling risk and making predictions based on Bayesian Network. Two case studies were used for the validation of our model. Results show the precision of our model especially in predicting the project finish time. This paper is organized as follows: in the next section, a survey of the related work and an overview of the Bayesian Network will be provided. Model Design is illustrated in section 3, while the validation is provided in section 4. Finally, conclusions are offered in the last section. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv available on arxiv Authors:
(1) Mohamed Abouelelam, Software System Engineering, University of Regina, Regina, Canada;
(2) Luigi Benedicenti, Software System Engineering, University of Regina, Regina, Canada. Authors: Authors: (1) Mohamed Abouelelam, Software System Engineering, University of Regina, Regina, Canada; (2) Luigi Benedicenti, Software System Engineering, University of Regina, Regina, Canada.

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