A Project Tracking Tool for Scrum Projects with Machine Learning Support for Cost Estimation
The design and implementation of a project tracking tool for software projects that are developed using the agile method Scrum is described and the tool supports cost estimation of the project based on user stories and sprint tasks.
Abstract
Cost estimation in software development is very important because it not only gives an idea to all stakeholders on how long it takes to complete the product under development, but it also mandates tracking development activities so that the project does not overrun on time or budget. Several cost estimation models have been reported in the literature for software development using traditional life cycle models but there are only a few ad hoc methods for software projects that used agile methods. This paper describes the design and implementation of a project tracking tool for software projects that are developed using the agile method Scrum. The users of the tool can closely monitor the progress of user stories, sprint tasks and test cases inducted into a scrum board. The tool also supports cost estimation of the project based on user stories and sprint tasks. For every user story, the tool provides a measure of hardship to implement in terms of story points, and for every sprint task, it gives the anticipated completion time. The tool uses machine learning support for continuous monitoring of efforts based on sprint tasks. The effectiveness of the tool has been tested using three different graduate course projects.