The Nexus between the Machine Learning Techniques and Software Project Estimation

Authors

  • Md. Shelim Miah Assistant Professor, Department of Business Administration, Asian University of Bangladesh, Dhaka, BANGLADESH
  • Mahesh Babu Pasupuleti Data Analyst, Department of IT, iMinds Technology Systems, Inc., Pittsburgh, PA 15243, USA
  • Harshini Priya Adusumalli Software Developer, CGI, 611 William Penn Pl # 1200, Pittsburgh, PA, USA

DOI:

https://doi.org/10.18034/gdeb.v10i1.627

Keywords:

Software Project, Software Estimation, Software Project Management, Machine Learning Technique

Abstract

Machine Learning is an application of artificial intelligence that allows computers to learn and develop without explicit programming. In other words, the goal of ML is to let computers learn on their own without human involvement and then alter their activities. ML also allows huge data processing. Project management planning and evaluation are vital in project execution. Project management is difficult without a realistic and logical plan. We give a complete overview of works on Machine Learning in Software Project Management. The first category contains software project management research articles. The third category includes research on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in production, and promotion of machine-learning project prediction. Our contribution also provides a broader viewpoint and context for future project risk management efforts. In conclusion, machine learning is more successful in reducing project failure probabilities, increasing output ratio for growth, and facilitating analysis on software fault prediction based on accuracy.

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References

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Published

2021-05-25

Issue

Vol. 10 No. 1 (2021): January - June Issue

Section

Articles

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How to Cite

Miah, M. S., Pasupuleti, M. B., & Adusumalli, H. P. (2021). The Nexus between the Machine Learning Techniques and Software Project Estimation. Global Disclosure of Economics and Business, 10(1), 37-44. https://doi.org/10.18034/gdeb.v10i1.627
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