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Improved Software Product Reliability Predictions using Machine Learning

Topics: Application Software; Big Data and Data Science; Data-driven Software Engineering; Empirical Software Engineering; Model-driven Software Engineering; Process Modeling; Project Management; Quality Management; Requirements Engineering; Software Development Lifecycle; Software Engineering Tools; Software Product Line; Software Project Planning and Tracking; Testing and Testability

Authors: Sanjay Joshi 1 and Yogesh Badhe 2

Affiliations: 1 Quality & Ops Excellence, Persistent Systems Ltd., Kashibai Khilare Street, Pune, India ; 2 Data Practice Group, Persistent Systems Ltd., Kashibai Khilare Street, Pune, India

Keyword(s): Software Reliability, SonarQube, Empirical Study, Experimentation, Correlation, Software Product, Reliability Prediction, Post-delivery Defects, Skill Level, Review Efficiency.

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.

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Paper citation in several formats:
Joshi, S. and Badhe, Y. (2021). Improved Software Product Reliability Predictions using Machine Learning. In Proceedings of the 16th International Conference on Software Technologies - ICSOFT, ISBN 978-989-758-523-4; ISSN 2184-2833, pages 245-252. DOI: 10.5220/0010576102450252

@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 - ICSOFT,},
year={2021},
pages={245-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010576102450252},
isbn={978-989-758-523-4},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - ICSOFT,
TI - Improved Software Product Reliability Predictions using Machine Learning
SN - 978-989-758-523-4
IS - 2184-2833
AU - Joshi, S.
AU - Badhe, Y.
PY - 2021
SP - 245
EP - 252
DO - 10.5220/0010576102450252