International Journal For Multidisciplinary Research

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Machine Learning-based Software Effort Estimation of Suggestive Agile and Scrumban Methodologies

Author(s) Srikanth, Dr. P.V. Bhaskar Reddy
Country India
Abstract The dynamic IT business and industries are welcoming the customer software requirements to adopt with new changes as per needs. With the recent advancement of technology in this era, software industries has grown outrageously. Software industry has shown such great hike in technology which is noncomparable to any other industries. Various methods have been established which improves the software quality one such method is Agile. Agile software development has gained a lot of attention because of its simplicity and ease of use. Agile software development is an approach which produces quality software with remarkable team interaction and more of customer involvement. Agile method is basically ideally suited for a scenario where requirements are changing in continuous manner. One of the most important advantage of using Agile is, it takes less time for software release, easy to understand and require less documentation. This research deals with various agile methods, their comparison, advantages, shortcomings and suggestive SCRUMBAN, a new propose is proposed.
Keywords SCRUMBAN, SRCUM, Extreme Programming, Feature Driven Development, Crystal, Adaptive Software development, Dynamic System Development Method
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 4, Issue 5, September-October 2022
Published On 2022-10-20
Cite This Machine Learning-based Software Effort Estimation of Suggestive Agile and Scrumban Methodologies - Srikanth, Dr. P.V. Bhaskar Reddy - IJFMR Volume 4, Issue 5, September-October 2022. DOI 10.36948/ijfmr.2022.v04i05.865
DOI https://doi.org/10.36948/ijfmr.2022.v04i05.865
Short DOI https://doi.org/grb58b
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