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.

Metrics

Metrics Loading ...

Downloads

Download data is not yet available.

References

Adusumalli, H. P. (2017a). Mobile Application Development through Design-based Investigation. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 14–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/58

Adusumalli, H. P. (2017b). Software Application Development to Backing the Legitimacy of Digital Annals: Use of the Diplomatic Archives. ABC Journal of Advanced Research, 6(2), 121-126. https://doi.org/10.18034/abcjar.v6i2.618

Adusumalli, H. P. (2018). Digitization in Agriculture: A Timely Challenge for Ecological Perspectives. Asia Pacific Journal of Energy and Environment, 5(2), 97-102. https://doi.org/10.18034/apjee.v5i2.619

Adusumalli, H. P. (2019). Expansion of Machine Learning Employment in Engineering Learning: A Review of Selected Literature. International Journal of Reciprocal Symmetry and Physical Sciences, 6, 15–19. Retrieved from https://upright.pub/index.php/ijrsps/article/view/65

Adusumalli, H. P., & Pasupuleti, M. B. (2017). Applications and Practices of Big Data for Development. Asian Business Review, 7(3), 111-116. https://doi.org/10.18034/abr.v7i3.597 DOI: https://doi.org/10.18034/mjmbr.v3i2.615

Donepudi, P. K., Ahmed, A. A. A., & Saha, S. (2020a). Emerging Market Economy (EME) and Artificial Intelligence (AI): Consequences for the Future of Jobs. PalArch's Journal of Archaeology of Egypt/ Egyptology, 17(6), 5562-5574. https://doi.org/10.5281/zenodo.5562662

Donepudi, P. K., Ahmed, A. A. A., Hossain, M. A., & Maria, P. (2020b). Perceptions of RAIA Introduction by Employees on Employability and Work Satisfaction in the Modern Agriculture Sector. International Journal of Modern Agriculture, 9(4), 486–497. https://doi.org/10.5281/zenodo.4428205

Donepudi, P. K., Banu, M. H., Khan, W., Neogy, T. K., Asadullah, ABM., & Ahmed, A. A. A. (2020c). Artificial Intelligence and Machine Learning in Treasury Management: A Systematic Literature Review. International Journal of Management, 11(11), 13–22. https://doi.org/10.5281/zenodo.4247297

Fadziso, T., Adusumalli, H. P., & Pasupuleti, M. B. (2018). Cloud of Things and Interworking IoT Platform: Strategy and Execution Overviews. Asian Journal of Applied Science and Engineering, 7, 85–92. Retrieved from https://upright.pub/index.php/ajase/article/view/63

Khan, W., Ahmed, A. A. A., Hossain, M. S., Neogy, T. K. (2020). The Interactive Approach to Working Capital Knowledge: Survey Evidence. International Journal of Nonlinear Analysis and Applications, 11(Special Issue), 379-393. https://doi.org/10.22075/ijnaa.2020.4631

Madding, C., Ansari, A., Ballenger, C., Thota, A. (2020). Topic Modeling to Understand Technology Talent. SMU Data Science Review, 3(2), 1-18.

Pasupuleti, M. B. (2016). Data Scientist Careers: Applied Orientation for the Beginners. Global Disclosure of Economics and Business, 5(2), 125-132. https://doi.org/10.18034/gdeb.v5i2.617 DOI: https://doi.org/10.18034/gdeb.v5i2.617

Pasupuleti, M. B. (2017). AMI Data for Decision Makers and the Use of Data Analytics Approach. Asia Pacific Journal of Energy and Environment, 4(2), 65-70. https://doi.org/10.18034/apjee.v4i2.623

Pasupuleti, M. B. (2018). The Application of Machine Learning Techniques in Software Project Management- An Examination. ABC Journal of Advanced Research, 7(2), 113-122. https://doi.org/10.18034/abcjar.v7i2.626

Pasupuleti, M. B. (2020). Artificial Intelligence and Traditional Machine Learning to Deep Neural Networks: A Study for Social Implications. Asian Journal of Humanity, Art and Literature, 7(2), 137-146. https://doi.org/10.18034/ajhal.v7i2.622

Pasupuleti, M. B., & Adusumalli, H. P. (2018). Digital Transformation of the High-Technology Manufacturing: An Overview of Main Blockades. American Journal of Trade and Policy, 5(3), 139-142. https://doi.org/10.18034/ajtp.v5i3.599

Pasupuleti, M. B., & Amin, R. (2018). Word Embedding with ConvNet-Bi Directional LSTM Techniques: A Review of Related Literature. International Journal of Reciprocal Symmetry and Physical Sciences, 5, 9–13. Retrieved from https://upright.pub/index.php/ijrsps/article/view/64

Pasupuleti, M. B., Miah, M. S., & Adusumalli, H. P. (2019). IoT for Future Technology Augmentation: A Radical Approach. Engineering International, 7(2), 105-116. https://doi.org/10.18034/ei.v7i2.601

Rahman, M. M., Chowdhury, M. R. H. K., Islam, M. A., Tohfa, M. U., Kader, M. A. L., Ahmed, A. A. A., & Donepudi, P. K. (2020). Relationship between Socio-Demographic Characteristics and Job Satisfaction: Evidence from Private Bank Employees. American Journal of Trade and Policy, 7(2), 65-72. https://doi.org/10.18034/ajtp.v7i2.492 DOI: https://doi.org/10.18034/ajtp.v7i2.492

Rahman, M. M., Pasupuleti, M. B., & Adusumalli, H. P. (2019). Advanced Metering Infrastructure Data: Overviews for the Big Data Framework. ABC Research Alert, 7(3), 159-168. https://doi.org/10.18034/abcra.v7i3.602

--0--

Downloads

Published

2021-05-25

Issue

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

Section

Articles

License

The CC BY-NC 4.0 license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge & be non-commercial, they don’t have to license their derivative works on the same terms.
|| View License Deed ||
|| View Legal Code ||
 
Publishing paper with Global Disclosure in Economics and Business means that the author or authors retain the copyright in the paper.
This journal is granted an exclusive noncommercial reuse license by the author(s), but the author(s) are able to put the paper onto a website, distribute it to colleagues, give it to students, use it in your thesis, etc, so long as the use is not directed at commercial advantage or toward private monetary gain.
The author(s) can reuse the figures and tables and other information contained in their paper published by Global Disclosure in Economics and Business in future papers or work without having to ask anyone for permission, provided that the figures, tables or other information that is included in the new paper or work properly references the published paper as the source of the figures, tables or other information, and the new paper or work is not direct at private monetary gain or commercial advantage.

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