Abstract
A carefully planned software development process helps in maintaining the quality of the software. In today's scenario the primitive software development models have been replaced by the Agile based models like SCRUM, KANBAN, LEAN, etc. Although, every framework has its own boon, the reason for widespread acceptance of the agile-based approach is its evolutionary nature that permits change in the path of software development. The development process occurs in iterative and incremental cycles called sprints. In SCRUM, which is one of the most widely used agile-based software development modeling framework; the sprint length is fixed throughout the process wherein; it is usually taken to be 1-4 weeks. But in practical application, the sprint length should be altered intuitively as per the requirement. To overcome this limitation, in this paper, a methodical work has been presented that determines the optimal sprint length based on two varied and yet connected attributes; the cost incurred and the work intensity required. The approach defines the number of tasks performed in each sprint along with the corresponding cost incurred in performing those tasks. Multi-attribute utility theory (MAUT), a multi-criterion decision making approach, has been utilized to find the required trade-off between two attributes under consideration. The proposed modeling framework has been validated using real life data set. With the use of the model, the optimal sprint for each sprint could be evaluated which was much shorter than the original length. Thus, the results obtained validate the proposal of a dynamic sprint length that can be determined before the start of each sprint. The structure would help in cost as well as time savings for a firm.