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Analysis of Ensemble Models for Aging Related Bug Prediction in Software Systems

Topics: Application Software; Artificial Intelligence Applications; Data-driven Software Engineering; Empirical Software Engineering; Quality Management; Software Development Lifecycle; Software Engineering Tools; Software Project Planning and Tracking; Testing and Testability

Authors: Shubham Sharma and Sandeep Kumar

Affiliation: Computer Science and Engineering Department, Indian Institute of Technology Roorkee, Uttarakhand, 247667 and India

Keyword(s): Aging Related Bugs, Software Fault Prediction, Software Aging, Stacking, Bagging, Boosting.

Abstract: With the evolution of the software industry, the growing software complexity led to the increase in the number of software faults. According to the study, the software faults are responsible for many unplanned system outages and affects the reputation of the company. Many techniques are proposed in order to avoid the software failures but still software failures are common. Many software faults and failures are outcomes of a phenomenon, called software aging. In this work, we have presented the use of various ensemble models for development of approach to predict the Aging Related Bugs (ARB). A comparative analysis of different ensemble techniques, bagging, boosting and stacking have been presented with their comparison with the base learning techniques which has not been explored in the prediction of ARBs. The experimental study has been performed on the LINUX and MYSQL bug datasets collected from Software Aging and Rejuvenation Repository.

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Paper citation in several formats:
Sharma, S. and Kumar, S. (2018). Analysis of Ensemble Models for Aging Related Bug Prediction in Software Systems. In Proceedings of the 13th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-320-9; ISSN 2184-2833, SciTePress, pages 256-263. DOI: 10.5220/0006847702900297

@conference{icsoft18,
author={Shubham Sharma. and Sandeep Kumar.},
title={Analysis of Ensemble Models for Aging Related Bug Prediction in Software Systems},
booktitle={Proceedings of the 13th International Conference on Software Technologies - ICSOFT},
year={2018},
pages={256-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006847702900297},
isbn={978-989-758-320-9},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - ICSOFT
TI - Analysis of Ensemble Models for Aging Related Bug Prediction in Software Systems
SN - 978-989-758-320-9
IS - 2184-2833
AU - Sharma, S.
AU - Kumar, S.
PY - 2018
SP - 256
EP - 263
DO - 10.5220/0006847702900297
PB - SciTePress

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