Cost-Sensitive Strategies for Data Imbalance in Bug Severity Classification: Experimental Results

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00100027" target="_blank" >RIV/00216224:14330/17:00100027 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/SEAA.2017.71" target="_blank" >http://dx.doi.org/10.1109/SEAA.2017.71</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SEAA.2017.71" target="_blank" >10.1109/SEAA.2017.71</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cost-Sensitive Strategies for Data Imbalance in Bug Severity Classification: Experimental Results

Popis výsledku v původním jazyce

Context: Software Bug Severity Classification can help to improve the software bug triaging process. However, severity levels present a high-level of data imbalance that needs to be taken into account. Aim: We investigate cost-sensitive strategies in multi-class bug severity classification to counteract data imbalance. Method: We transform datasets from three severity classification papers to a common format, totaling 17 projects. We test different cost sensitive strategies to penalize majority classes. We adopt a Support Vector Machine (SVM) classifier that we also compare to a baseline "majority class" classifier. Results: A model weighting classes based on the inverse of instance frequencies yields a statistically significant improvement (low effect size) over the standard unweighted SVM model in the assembled dataset. Conclusions: Data imbalance should be taken more into consideration in future severity classification research papers.

Název v anglickém jazyce

Cost-Sensitive Strategies for Data Imbalance in Bug Severity Classification: Experimental Results

Popis výsledku anglicky

Context: Software Bug Severity Classification can help to improve the software bug triaging process. However, severity levels present a high-level of data imbalance that needs to be taken into account. Aim: We investigate cost-sensitive strategies in multi-class bug severity classification to counteract data imbalance. Method: We transform datasets from three severity classification papers to a common format, totaling 17 projects. We test different cost sensitive strategies to penalize majority classes. We adopt a Support Vector Machine (SVM) classifier that we also compare to a baseline "majority class" classifier. Results: A model weighting classes based on the inverse of instance frequencies yields a statistically significant improvement (low effect size) over the standard unweighted SVM model in the assembled dataset. Conclusions: Data imbalance should be taken more into consideration in future severity classification research papers.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2017

ISBN

9781538621400

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

426-429

Název nakladatele

IEEE

Místo vydání

Not specified

Místo konání akce

Vienna

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426074600063