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 3.3. Defected Story Points Model This model calculates an estimate number for the defected story points to be redeveloped in the next release. This number is affected by two XP practices: Test Driven development and Onsite Customer practices. Different components of the model are described as follows: Dev. Productivity: The developer productivity measured as the number Line Of Code (LOC) per day. According to the literature [4], a normal distribution with mean 40 and Standard Deviation of 20 represents this value.


Estimated Release KLOC: represents the number of KLOC produced from this release. This value is calculated as the product of multiplying Dev. Productivity times Team size times Estimated Release Days.


Defect Injection Ratio: represents the number of defects per KLOC. This value was set to a normal distribution with mean 20 and standard deviation 5 [4].


Defect Rate: represents the number of defects in this release. It is calculated as the multiplication of the Estimated Release KLOC times Defect Injection Ratio.


Defected Story Points: This value represents the number of defected story points to be re-developed in the next release taking into account the impact of two XP practices: Test Driven development and Onsite Customer practices (Equation 3). OSC_Impact_Factor and TDD_Impact_Factor represent the impact of the Onsite Customer and Test Driven development practices on reducing the defect rate. According to the literature, there values were set to 0.8 and 0.4 respectively [3],[4]. More details regarding the impact of these practices in the defect rate are available in the Background section. Dev. Productivity: The developer productivity measured as the number Line Of Code (LOC) per day. According to the literature [4], a normal distribution with mean 40 and Standard Deviation of 20 represents this value. Dev. Productivity: The developer productivity measured as the number Line Of Code (LOC) per day. According to the literature [4], a normal distribution with mean 40 and Standard Deviation of 20 represents this value. Estimated Release KLOC: represents the number of KLOC produced from this release. This value is calculated as the product of multiplying Dev. Productivity times Team size times Estimated Release Days. Estimated Release KLOC: represents the number of KLOC produced from this release. This value is calculated as the product of multiplying Dev. Productivity times Team size times Estimated Release Days. Team size Estimated Release Days. Defect Injection Ratio: represents the number of defects per KLOC. This value was set to a normal distribution with mean 20 and standard deviation 5 [4]. Defect Injection Ratio: represents the number of defects per KLOC. This value was set to a normal distribution with mean 20 and standard deviation 5 [4]. Defect Rate: represents the number of defects in this release. It is calculated as the multiplication of the Estimated Release KLOC times Defect Injection Ratio. Defect Rate: represents the number of defects in this release. It is calculated as the multiplication of the Estimated Release KLOC times Defect Injection Ratio. Estimated Release KLOC Defect Injection Ratio. Defected Story Points: This value represents the number of defected story points to be re-developed in the next release taking into account the impact of two XP practices: Test Driven development and Onsite Customer practices (Equation 3). OSC_Impact_Factor and TDD_Impact_Factor represent the impact of the Onsite Customer and Test Driven development practices on reducing the defect rate. According to the literature, there values were set to 0.8 and 0.4 respectively [3],[4]. More details regarding the impact of these practices in the defect rate are available in the Background section. Defected Story Points: This value represents the number of defected story points to be re-developed in the next release taking into account the impact of two XP practices: Test Driven development and Onsite Customer practices (Equation 3). OSC_Impact_Factor and TDD_Impact_Factor represent the impact of the Onsite Customer and Test Driven development practices on reducing the defect rate. According to the literature, there values were set to 0.8 and 0.4 respectively [3],[4]. More details regarding the impact of these practices in the defect rate are available in the Background section. OSC_Impact_Factor TDD_Impact_Factor Defected_Story_Points = Defect_Rate*(1- OSC_Impact_Factor * onsitecustomer_usage )*(1 TDD_Impact_Factor *tddusage) Equation (3) Defected_Story_Points = Defect_Rate*(1- OSC_Impact_Factor * onsitecustomer_usage )*(1 TDD_Impact_Factor *tddusage) 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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