Ensemble of software defect predictors: A case study

Ayse Tosun; Burak Turhan; Ayse Bener

doi:10.1145/1414004.1414066

Ensemble of software defect predictors: A case study

Ayse Tosun, Burak Turhan, Ayse Bener

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

35 Citations (Scopus)

Abstract

In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.

Original language	English
Title of host publication	ESEM'08
Subtitle of host publication	Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Pages	318-320
Number of pages	3
DOIs	https://doi.org/10.1145/1414004.1414066
Publication status	Published - 1 Dec 2008
Externally published	Yes
Event	2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany Duration: 9 Oct 2008 → 10 Oct 2008

Conference

Conference	2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008
Country/Territory	Germany
City	Kaiserslautern
Period	9/10/08 → 10/10/08

Keywords

Defect prediction
Ensemble of classifiers
Static code attributes

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10.1145/1414004.1414066

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title = "Ensemble of software defect predictors: A case study",

abstract = "In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.",

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Tosun, A, Turhan, B & Bener, A 2008, Ensemble of software defect predictors: A case study. in ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. pp. 318-320, 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008, Kaiserslautern, Germany, 9/10/08. https://doi.org/10.1145/1414004.1414066

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AU - Tosun, Ayse

AU - Turhan, Burak

AU - Bener, Ayse

PY - 2008/12/1

Y1 - 2008/12/1

N2 - In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.

AB - In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.

KW - Defect prediction

KW - Ensemble of classifiers

KW - Static code attributes

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DO - 10.1145/1414004.1414066

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Y2 - 9 October 2008 through 10 October 2008

ER -

Ensemble of software defect predictors: A case study

Abstract

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Keywords

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