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Authors: Pravas Ranjan Bal and Sandeep Kumar

Affiliation: Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee and India

Keyword(s): Extreme Learning Machine, Ensemble Model, Inter Release Prediction, Within Project Defect Prediction.

Abstract: Many recent studies have experimented the software fault prediction models to predict the number of software faults using statistical and traditional machine learning techniques. However, it is observed that the performance of traditional software fault prediction models vary from dataset to dataset. In addition, the performance of the traditional models degrade for inter release prediction. To address these issues, we have proposed linear homogeneous ensemble methods based on two variations of extreme learning machine, Differentiable Extreme Learning Machine Ensemble (DELME) and Non-differentiable Extreme Learning Machine Ensemble (NELME), to predict the number of software faults. We have used seventeen PROMISE datasets and five eclipse datasets to validate these software fault prediction models. We have performed two types of predictions, within project defect prediction and inter release prediction, to validate our proposed fault prediction model. The experimental result shows con sistently better performance across all datasets. (More)

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Paper citation in several formats:
Bal, P. and Kumar, S. (2018). Extreme Learning Machine based Linear Homogeneous Ensemble for Software Fault Prediction. In Proceedings of the 13th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-320-9; ISSN 2184-2833, SciTePress, pages 69-78. DOI: 10.5220/0006839501030112

@conference{icsoft18,
author={Pravas Ranjan Bal. and Sandeep Kumar.},
title={Extreme Learning Machine based Linear Homogeneous Ensemble for Software Fault Prediction},
booktitle={Proceedings of the 13th International Conference on Software Technologies - ICSOFT},
year={2018},
pages={69-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006839501030112},
isbn={978-989-758-320-9},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - ICSOFT
TI - Extreme Learning Machine based Linear Homogeneous Ensemble for Software Fault Prediction
SN - 978-989-758-320-9
IS - 2184-2833
AU - Bal, P.
AU - Kumar, S.
PY - 2018
SP - 69
EP - 78
DO - 10.5220/0006839501030112
PB - SciTePress

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