loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Burak Turhan and Ayşe Bener

Affiliation: Bogazici University, Turkey

Keyword(s): Empirical software engineering, Software quality, Defect prediction, Software metrics, Naïve Bayes, Feature weighting.

Related Ontology Subjects/Areas/Topics: Enterprise Software Technologies ; Reliable Software Technologies ; Software Engineering

Abstract: Defect prediction is an important topic in software quality research. Statistical models for defect prediction can be built on project repositories. Project repositories store software metrics and defect information. This information is then matched with software modules. Naïve Bayes is a well known, simple statistical technique that assumes the ‘independence’ and ‘equal importance’ of features, which are not true in many problems. However, Naïve Bayes achieves high performances on a wide spectrum of prediction problems. This paper addresses the ‘equal importance’ of features assumption of Naïve Bayes. We propose that by means of heuristics we can assign weights to features according to their importance and improve defect prediction performance. We compare the weighted Naïve Bayes and the standard Naïve Bayes predictors’ performances on publicly available datasets. Our experimental results indicate that assigning weights to software metrics increases the prediction performance signif icantly. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.117.152.251

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Turhan, B. and Bener, A. (2007). SOFTWARE DEFECT PREDICTION: HEURISTICS FOR WEIGHTED NAÏVE BAYES. In Proceedings of the Second International Conference on Software and Data Technologies - Volume 1: ICSOFT; ISBN 978-989-8111-06-7; ISSN 2184-2833, SciTePress, pages 244-249. DOI: 10.5220/0001339402440249

@conference{icsoft07,
author={Burak Turhan. and Ayşe Bener.},
title={SOFTWARE DEFECT PREDICTION: HEURISTICS FOR WEIGHTED NAÏVE BAYES},
booktitle={Proceedings of the Second International Conference on Software and Data Technologies - Volume 1: ICSOFT},
year={2007},
pages={244-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001339402440249},
isbn={978-989-8111-06-7},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the Second International Conference on Software and Data Technologies - Volume 1: ICSOFT
TI - SOFTWARE DEFECT PREDICTION: HEURISTICS FOR WEIGHTED NAÏVE BAYES
SN - 978-989-8111-06-7
IS - 2184-2833
AU - Turhan, B.
AU - Bener, A.
PY - 2007
SP - 244
EP - 249
DO - 10.5220/0001339402440249
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic