Abstract
Abstract: Software Process Models from its inception instill standardization and creates a generic culture of developing software for various IT industries. A great paradigm shift has been observed in terms of embracing Agile Development methodology as a viable development methodology in cross key business units. There is a buffet of agile methodologies comes under the umbrella of ASD, out of which Scrum got the highest popularity and acceptability index. Agile based software development is the need of immediate environment. There is an increasing demand for significant changes to software systems to meet ever-changing user requirements and specifications. As Agile is volatile, so effort estimation is challenging and still striving for perfection to decide size, effort, cost, duration and schedule of projects with minimum error. This cause sensitizes potential researchers all across the globe to start working on addressing the issue of inaccurate predication of efforts. The gap between estimated and actual effort is because of limited or no inclusion of various estimation factors like people and project related factors, inappropriate use of size metric and cost drivers, ignorance of testing effort, team member’s inability to understand user story size and complexity etc. This paper attempts to bridge the gap of estimated and actual effort by the use of soft computing techniques thus taking the research to advance frontier area in terms ofestimation. Keywords: Cost Estimation, Effort Estimation, Scrum, Machine Learning, Agile Software Development
Published Version
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