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Authors: Camelia Şerban and Andreea Vescan

Affiliation: Department of Computer Science, Babeş-Bolyai University, M. Kogalniceanu 1, Cluj-Napoca, Romania

Keyword(s): Reliability, Metrics, Assessment, Prediction, Neural Network, Object-oriented Design.

Abstract: Nowadays, software systems have become larger and more complex than ever. A system failure could threaten the safety of human life. Discovering the bugs as soon as possible during the software development and investigating the effect of a change in the software system are two main concerns of the software developers to increase system’s reliability. Our approach employs a neural network to predict reliability via post-release defects and changes applied during the software development life cycle. The CK metrics are used as predictors variables, whereas the target variable is composed of both bugs and changes having different weights. This paper empirically investigates various prediction models considering different weights for the components of the target variable using five open source projects. Two major perspectives are explored: cross-project to identify the optimum weight values for bugs and changes and cross-project to discover the best training project for a selected weight. The results show that for both cross-project experiments, the best accuracy is obtained for the models with the highest weights for bugs (75% bugs and 25% changes) and that the right fitted project to be used as training is the PDE project. (More)

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Paper citation in several formats:
Şerban, C. and Vescan, A. (2021). Towards a Neural Network based Reliability Prediction Model via Bugs and Changes. In Proceedings of the 16th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-523-4; ISSN 2184-2833, SciTePress, pages 302-309. DOI: 10.5220/0010600703020309

@conference{icsoft21,
author={Camelia Şerban. and Andreea Vescan.},
title={Towards a Neural Network based Reliability Prediction Model via Bugs and Changes},
booktitle={Proceedings of the 16th International Conference on Software Technologies - ICSOFT},
year={2021},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010600703020309},
isbn={978-989-758-523-4},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Software Technologies - ICSOFT
TI - Towards a Neural Network based Reliability Prediction Model via Bugs and Changes
SN - 978-989-758-523-4
IS - 2184-2833
AU - Şerban, C.
AU - Vescan, A.
PY - 2021
SP - 302
EP - 309
DO - 10.5220/0010600703020309
PB - SciTePress

- Science and Technology Publications, Lda.
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