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

Shubham Sharma, Sandeep Kumar

2018

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICSOFT, ISBN 978-989-758-320-9, pages 256-263. DOI: 10.5220/0006847702560263

in Bibtex Style

@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 - Volume 1: ICSOFT,},
year={2018},
pages={256-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006847702560263},
isbn={978-989-758-320-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Analysis of Ensemble Models for Aging Related Bug Prediction in Software Systems
SN - 978-989-758-320-9
AU - Sharma S.
AU - Kumar S.
PY - 2018
SP - 256
EP - 263
DO - 10.5220/0006847702560263