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International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.7, No.3, May. 2017

An Investigation on the Metric Threshold for Fault- Proneness

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Author(s)

Aarti, Geeta Sikka, Renu Dhir

Index Terms

Quality;semi-supervised;fault- proneness;false-alarm

Abstract

The software quality can be enhanced with the awareness and compassionate about the software faults. We acknowledge the impact of threshold of the object-oriented metrics on fault-proneness. The prediction of fault-prone classes in early stage of the life-cycle assures you to allocate the resources effectively. In this paper, we proposed the logistic regression based statistical method and metric threshold to reduce the false alarm for projects that fall outside the risk range. We presented the threshold effects on public datasets collected from the NASA repository and validated the use of threshold on ivy and jedit datasets. The results concluded that proposed methodology achieves the speculative results with projects having similar characteristic.

Cite This Paper

Aarti, Geeta Sikka, Renu Dhir,"An Investigation on the Metric Threshold for Fault- Proneness", International Journal of Education and Management Engineering(IJEME), Vol.7, No.3, pp.35-42, 2017.DOI: 10.5815/ijeme.2017.03.04

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