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 3.2. Team Velocity Model Team velocity, measured in number of user stories per day, is a good representation for the productivity of the team. The model is shown in Figure 4. Three main factors affect the team velocity in XP, namely: Team XP skills, Pair Programming usage and Test Driven Development usage. user stories The main components of the model are: Developer initial skills: random number uniformly distributed over the range [1,10] [4]. This number represents the developer ability to develop software.


Developer skills: Dev. Skills depend on two main factor, namely: dev_initial_Skills and project_working_days. This value represents increasing developer skills with time. It can be calculated as described in Equation (1)[4], where LC is the learning coefficient, typically 0.009 [4]. Developer initial skills: random number uniformly distributed over the range [1,10] [4]. This number represents the developer ability to develop software. Developer initial skills: random number uniformly distributed over the range [1,10] [4]. This number represents the developer ability to develop software. Developer skills: Dev. Skills depend on two main factor, namely: dev_initial_Skills and project_working_days. This value represents increasing developer skills with time. It can be calculated as described in Equation (1)[4], where LC is the learning coefficient, typically 0.009 [4]. Developer skills: Dev. Skills depend on two main factor, namely: dev_initial_Skills and project_working_days. This value represents increasing developer skills with time. It can be calculated as described in Equation (1)[4], where LC is the learning coefficient, typically 0.009 [4]. dev_initial_Skills project_working_days. LC Dev_skills = log(dev_initial_Skills + project_working_days*LC) Equation (1) Dev_skills = log(dev_initial_Skills + project_working_days*LC) Developer Initial Velocity: represents the number of story points the user can develop per day. According to empirical data [4], a random variable following the normal distribution with mean 4 and standard deviation 1 was considered to represent this value.


Dev. Velocity: this value represents the impact of the developer skills on his velocity. It is calculated as the summation of the Dev. Initial Velocity and the dev. Skills factor.


Team Initial Velocity: represents the number of story points the team can develop per day. It can be calculated as the product of Team Size times Dev. Velocity.


Pair Programming Impact Factor (pp_Impactfactor): This factor represents the impact of adopting Pair Programming practices in the team velocity. This value was set to a normal distribution random variable with mean 23 and standard deviation 20. This value was set according to a number of studies. Those studies show that Pair Programming reduces the Project time by a factor starting from 0 (no change) in some cases to 45% in others. More data regarding those studies is available in the Background section.


Test Driven development Impact Factor (TDD_Impactfactor): This factor represents the impact of adopting Test Driven Development practices in the team velocity. This value was set to a normal distribution random variable with mean -32 and standard deviation 42. This value was calculated according to number of studies [4]. Those studies show that Test Driven Development results in increasing the project time by a factor starting from 0 (no increase) in some studies to 80% in others. A review of those studies is available in the Background section.


Team Velocity: This value represents the number of story points the team can develop per day taking into account all the previous factors. Equation 2 represents this value. Developer Initial Velocity: represents the number of story points the user can develop per day. According to empirical data [4], a random variable following the normal distribution with mean 4 and standard deviation 1 was considered to represent this value. Developer Initial Velocity: represents the number of story points the user can develop per day. According to empirical data [4], a random variable following the normal distribution with mean 4 and standard deviation 1 was considered to represent this value. Dev. Velocity: this value represents the impact of the developer skills on his velocity. It is calculated as the summation of the Dev. Initial Velocity and the dev. Skills factor. Dev. Velocity: this value represents the impact of the developer skills on his velocity. It is calculated as the summation of the Dev. Initial Velocity and the dev. Skills factor. Skills Team Initial Velocity: represents the number of story points the team can develop per day. It can be calculated as the product of Team Size times Dev. Velocity. Team Initial Velocity: represents the number of story points the team can develop per day. It can be calculated as the product of Team Size times Dev. Velocity. Velocity Pair Programming Impact Factor (pp_Impactfactor): This factor represents the impact of adopting Pair Programming practices in the team velocity. This value was set to a normal distribution random variable with mean 23 and standard deviation 20. This value was set according to a number of studies. Those studies show that Pair Programming reduces the Project time by a factor starting from 0 (no change) in some cases to 45% in others. More data regarding those studies is available in the Background section. Pair Programming Impact Factor (pp_Impactfactor): This factor represents the impact of adopting Pair Programming practices in the team velocity. This value was set to a normal distribution random variable with mean 23 and standard deviation 20. This value was set according to a number of studies. Those studies show that Pair Programming reduces the Project time by a factor starting from 0 (no change) in some cases to 45% in others. More data regarding those studies is available in the Background section. pp_Impactfactor Test Driven development Impact Factor (TDD_Impactfactor): This factor represents the impact of adopting Test Driven Development practices in the team velocity. This value was set to a normal distribution random variable with mean -32 and standard deviation 42. This value was calculated according to number of studies [4]. Those studies show that Test Driven Development results in increasing the project time by a factor starting from 0 (no increase) in some studies to 80% in others. A review of those studies is available in the Background section. Test Driven development Impact Factor (TDD_Impactfactor): This factor represents the impact of adopting Test Driven Development practices in the team velocity. This value was set to a normal distribution random variable with mean -32 and standard deviation 42. This value was calculated according to number of studies [4]. Those studies show that Test Driven Development results in increasing the project time by a factor starting from 0 (no increase) in some studies to 80% in others. A review of those studies is available in the Background section. TDD_Impactfactor Team Velocity: This value represents the number of story points the team can develop per day taking into account all the previous factors. Equation 2 represents this value. Team Velocity: This value represents the number of story points the team can develop per day taking into account all the previous factors. Equation 2 represents this value. Team Velocity = Team_initial_velocity* (1+PP_usage*PP_ImpactFactor/100)* (1+TDD_usage*TDD_impactfactor/100) Equation (2) (1+TDD_usage*TDD_impactfactor/100) 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

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

How to Calculate Team Velocity in an XP Project

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Dynamic Programming Approach to Optimizing Signaling Strategies in Multi-phase Trials:

EffConPy: Open Source Causality Discovery in Python

Predicting Software Development with XP: A Look at Existing Models

The Role of Onsite Customers in Agile Software Development

Understanding Bayesian Networks: A Graphical Model for Uncertainty

The Iterative Structure of an XP Process Model

A Dynamic Programming Approach to Optimizing Signaling Strategies in Multi-phase Trials:

EffConPy: Open Source Causality Discovery in Python

Predicting Software Development with XP: A Look at Existing Models

The Role of Onsite Customers in Agile Software Development

Understanding Bayesian Networks: A Graphical Model for Uncertainty

The Iterative Structure of an XP Process Model

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps