Machine Learning Based Approach for Evaluating Agile Based Methods to Enhance Software Quality
Neha Saini1, Indu Chhabra2, Ajay Guleria3
1Neha Saini, Research Scholar, Department of Computer Science and Applications, Panjab University, Panjab (Chandigarh), India.
2Prof. Indu Chhabra, Professor, Department of Computer Science and Applications, Panjab University, Panjab (Chandigarh), India.
3Dr. Ajay Guleria, System Manager, Computer Services Centre, IIT Delhi. India.

Manuscript received on 01 December 2022 | Revised Manuscript received on 07 December 2022 | Manuscript Accepted on 15 December 2022 | Manuscript published on 30 December 2022. | PP: 123-127 | Volume-12 Issue-2, December 2022 | Retrieval Number: 100.1/ijeat.B39561212222 | DOI: 10.35940/ijeat.B3956.1212222
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Abstract: Developing a quality software product is an essential need of the software industry. Software quality comprises of various factors. Therefore, it cannot be measured on the basis of a single variable. Several agile software development methods have evolved all around the world with the passage of time that contribute towards the development of new and improved software methods. The agile processes have started invading the software development industry to provide good quality software in minimal time. As the changes have occurred in the modern day evaluation metrics, the changes have been observed in the agile oriented quality evaluation methods as well. This paper presents a machine learning based approach for evaluating agile based methods for enhancing software quality. This advanced mechanism of processing the data attributes is inspired by SWARA and FDD. The validation and evaluation has been done using statistical and the quantitative parameters.
Keywords: Feature Selection, Software Quality, Algorithm, Agile Methodologies, Software Development

Scope of the Article: Machine Learning.