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 2. BACKGROUND In the next section, a literature survey of related work will be provided. The survey covers a number of the most important XP process models existing in the literature. Then, the Impact of XP Practices on Software Productivity and Quality is illustrated in the following section. Finally, an overview of the Bayesian Network will be provided in the last section. 2.1. Related Work Although the widespread usage of XP in both academic and industry, only few attempts for modelling XP exists. In this section, we will provide a survey of the most important XP Process models. In [2], a simulation model was developed to analyse the effects of almost all XP practices on the software development effort. The developed model was applied for a typical XP project and the effects of all the individual practices were calculated. The results showed a reduction in software development cost as increasing the usage levels of all XP practices. In [3], the authors built a software effort prediction model for XP based on Bayesian networks. The proposed model can learn from project data in order to make quantitative effort predictions and risk assessments. The model has been validated by applying to a real industrial project. Collecting data from the early part of the project enabled the model to update its parameters and improve its predictions. The model could successfully achieve extremely accurate predictions about the level of functionality delivered over time. In [4], the authors introduced an XP process model to evaluate the effectiveness of XP key practices (Pair Programming, Test-First Programming), and to investigate how the practices influence the evolution of a certain project. To achieve this, software process simulation has been chosen. A process model has been developed and a simulation executive has been implemented to enable simulation of XP software development activities to simulate how the modeled project entities evolve as a result. Williams and Erdogmus [5] developed a Net Present Value (NPV) model of Pair Programming (PP). The model combines the productivity rates, code production rates, defect insertion rates, and defect removal rates. The authors conclude that PP is a “viable alternative to individual programming.” In [6], the authors model, simulate and analyze the pair programming and pair switching practices. The model explores many variables affecting pair programming efficiency. The results showed that XP efficiency increases with both psychological compatibility and pair adaptation speed between the members of the pair. In addition, the XP process appears to have an advantage over the traditional approach when pair switches are not too frequent. 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. 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

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