A Project Tracking Tool for Scrum Projects with Machine Learning Support for Cost Estimation

9 pages•Published: March 1, 2021

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.

Keyphrases: cost estimation, project tracking, scrum

In: Alex Redei, Rui Wu and Frederick Harris (editors). SEDE 2020. 29th International Conference on Software Engineering and Data Engineering, vol 76, pages 86-94.

Links:	https://easychair.org/publications/paper/BHXq
	https://doi.org/10.29007/6vwh

BibTeX entry

@inproceedings{SEDE2020:Project_Tracking_Tool_Scrum,
  author    = {Kasi Periyasamy and Joshua Chianelli},
  title     = {A Project Tracking Tool for Scrum Projects with Machine Learning Support for Cost Estimation},
  booktitle = {SEDE 2020. 29th International Conference on Software Engineering and Data Engineering},
  editor    = {Alex Redei and Rui Wu and Frederick Harris},
  series    = {EPiC Series in Computing},
  volume    = {76},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/BHXq},
  doi       = {10.29007/6vwh},
  pages     = {86-94},
  year      = {2021}}

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