Details

Autor(en) / Beteiligte

• Iftikhar, Asim
• Alam, Muhammad
• Ahmed, Rizwan
• Musa, Shahrulniza
• Su’ud, Mazliham Mohd
• Yi, Yugen
• Yugen Yi

Titel

Risk Prediction by Using Artificial Neural Network in Global Software Development

Ist Teil von

• Computational intelligence and neuroscience, 2021, Vol.2021 (1), p.2922728-2922728

Ort / Verlag

United States: Hindawi

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.