Improved Software Product Reliability Predictions using Machine Learning

Sanjay Joshi, Yogesh Badhe

2021

Abstract

Reliability is one of the key attributes of software product quality. Popular software reliability prediction models are targeted to specific phases of software product development life cycle. After studying, reliability models, authors could conclude that they have limitations in predicting software product reliability. A recent industrial survey performed by the authors identified several factors which practitioners perceived to have influence in predicting reliability. Subsequently authors conducted set of experiments to find out influential factors to reliability. In this paper, authors presented model definition approach using most influential parameters such as review efficiency, skill level of developer/tester and post-delivery defects.

Paper Citation

in Harvard Style

Joshi S. and Badhe Y. (2021). Improved Software Product Reliability Predictions using Machine Learning. In Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-523-4, pages 245-252. DOI: 10.5220/0010576102450252

in Bibtex Style

@conference{icsoft21,
author={Sanjay Joshi and Yogesh Badhe},
title={Improved Software Product Reliability Predictions using Machine Learning},
booktitle={Proceedings of the 16th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2021},
pages={245-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010576102450252},
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 - Improved Software Product Reliability Predictions using Machine Learning
SN - 978-989-758-523-4
AU - Joshi S.
AU - Badhe Y.
PY - 2021
SP - 245
EP - 252
DO - 10.5220/0010576102450252