An Ensemble Classifier based Method for Effective Fault Localization

Arpita Dutta, Rajib Mall

2022

Abstract

Fault localization (FL) is one of the most difficult and tedious task during software debugging. It has been reported in literature that different FL techniques show superior results under different circumstances. No reported technique always outperforms all existing FL techniques for each type of bug. On the other hand, it has been reported that ensemble classifiers combine different learning methods to obtain better predictive performance that may not be obtained from any of the constituent learning algorithms alone. This has motivated us to use an ensemble classifier for effective fault localization. We focus on three different families of fault localization techniques, viz., spectrum based (SBFL), mutation based (MBFL), and neural-network based (NNBFL) to achieve this. In total, we have considered eleven representative methods from these three families of FL techniques. Our underlying model is simple and intuitive as it is based only on the statement coverage data and test execution results. Our proposed ensemble classifier based FL (EBFL) method classifies the statements into two different classes viz., Suspicious and Non-Suspicious set of statements. This helps to reduce the search space significantly. Our experimental results show that our proposed EBFL technique requires, on an average, 58% of less code examination as compare to the other contemporary FL techniques, viz., Tarantula, DStar, CNN, DNN etc.

Paper Citation

in Harvard Style

Dutta A. and Mall R. (2022). An Ensemble Classifier based Method for Effective Fault Localization. In Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-588-3, pages 159-166. DOI: 10.5220/0011166800003266

in Bibtex Style

@conference{icsoft22,
author={Arpita Dutta and Rajib Mall},
title={An Ensemble Classifier based Method for Effective Fault Localization},
booktitle={Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2022},
pages={159-166},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011166800003266},
isbn={978-989-758-588-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - An Ensemble Classifier based Method for Effective Fault Localization
SN - 978-989-758-588-3
AU - Dutta A.
AU - Mall R.
PY - 2022
SP - 159
EP - 166
DO - 10.5220/0011166800003266