The Nexus between the Machine Learning Techniques and Software Project Estimation
DOI:
https://doi.org/10.18034/gdeb.v10i1.627Keywords:
Software Project, Software Estimation, Software Project Management, Machine Learning TechniqueAbstract
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.
Downloads
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--