Software Task Importance Prediction based on Project Management Data

Themistoklis Diamantopoulos, Christiana Galegalidou, Andreas Symeonidis

2021

Abstract

With the help of project management tools and code hosting facilities, software development has been transformed into an easy-to-decentralize business. However, determining the importance of tasks within a software engineering process in order to better prioritize and act on has always been an interesting challenge. Although several approaches on bug severity/priority prediction exist, the challenge of task importance prediction has not been sufficiently addressed in current research. Most approaches do not consider the meta-data and the temporal characteristics of the data, while they also do not take into account the ordinal characteristics of the importance/severity variable. In this work, we analyze the challenge of task importance prediction and propose a prototype methodology that extracts both textual (titles, descriptions) and meta-data (type, assignee) characteristics from tasks and employs a sliding window technique to model their time frame. After that, we evaluate three different prediction methods, a multi-class classifier, a regression algorithm, and an ordinal classification technique, in order to assess which model is the most effective for encompassing the relative ordering between different importance values. The results of our evaluation are promising, leaving room for future research.

Paper Citation

in Harvard Style

Diamantopoulos T., Galegalidou C. and Symeonidis A. (2021). Software Task Importance Prediction based on Project Management Data. In Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-523-4, pages 269-276. DOI: 10.5220/0010578302690276

in Bibtex Style

@conference{icsoft21,
author={Themistoklis Diamantopoulos and Christiana Galegalidou and Andreas Symeonidis},
title={Software Task Importance Prediction based on Project Management Data},
booktitle={Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2021},
pages={269-276},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010578302690276},
isbn={978-989-758-523-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Software Task Importance Prediction based on Project Management Data
SN - 978-989-758-523-4
AU - Diamantopoulos T.
AU - Galegalidou C.
AU - Symeonidis A.
PY - 2021
SP - 269
EP - 276
DO - 10.5220/0010578302690276