Table of Links
Abstract and 1. Introduction
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Background and 2.1. Related Work
2.2. The Impact of XP Practices on Software Productivity and Quality
2.3. Bayesian Network Modelling
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Model Design
3.1. Model Overview
3.2. Team Velocity Model
3.3. Defected Story Points Model
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Model Validation
4.1. Experiments Setup
4.2. Results and Discussion
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Conclusions and References
5. CONCLUSIONS
In this paper, a Bayesian Network based mathematical model for XP process is presented. The model can be used to predict the success/ failure of any XP project by estimating the expected finish time and the expected defect rate for each XP release. The proposed model comprises two internal models: Team velocity and Defected Story Points models. The model takes into account the impact of XP practices.
Two case studies were used for the validation of our model, namely: Repo Margining System and Abrahamsson Case Study. The results show that the model can be used successfully to predict the project finish time with a reasonable accuracy in the project planning phase using very simple input data. In addition, the results show that the accuracy of the Team Velocity Model is acceptable, while the Defected Story Points model was not that accurate.
Adopting the model to have a self-learning capability is a good extension of this work and can solve the imprecision in some of the results, especially in the Defected Story Points model, by which the model can learn from the first iterations and adjust different parameters and variables. This increases the confidence of the prediction and can correct the model’s prior assumptions. This learning capability is a good extension for the proposed model.
REFERENCES
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Authors
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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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This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
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