Discriminating features-based cost-sensitive approach for software defect prediction

Aftab Ali; Naveed Khan; Mamun Abu-Tair; Joost Noppen; Sally I McClean; Ian McChesney

doi:10.1007/s10515-021-00289-8

Discriminating features-based cost-sensitive approach for software defect prediction

Aftab Ali, Naveed Khan, Mamun Abu-Tair, Joost Noppen, Sally I McClean, Ian McChesney

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

87 Downloads (Pure)

Abstract

Correlated quality metrics extracted from a source code repository
can be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highly
unbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a fact
that the selection of the best discriminating features significantly improves
the robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating features
that help in accurately predicting a defect in a software component. Secondly,
a cost-sensitive logistic regression and decision tree ensemble-based prediction
models are applied to the best discriminating features for precisely predicting
a defect in a software component. The proposed models are compared with
the most recent schemes in the literature in terms of accuracy, area under thecurve (AUC), and recall. The models are evaluated using 11 datasets and it
is evident from the results and analysis that the performance of the proposed
prediction models outperforms the schemes in the literature.

Original language	English
Article number	11
Number of pages	18
Journal	Automated software engineering
Volume	28
Early online date	12 Jul 2021
DOIs	https://doi.org/10.1007/s10515-021-00289-8
Publication status	Published online - 12 Jul 2021

Bibliographical note

Funding Information:
This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.

Publisher Copyright:
© 2021, The Author(s).

Keywords

AUC
Cost-sensitivity
Discriminating features
Machine learning models
Recall
Software bugs/defects

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s10515-021-00289-8Licence: CC BY

Ali2021_Article_DiscriminatingFeatures-basedCoFinal published version, 1.25 MBLicence: CC BY

Cite this

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title = "Discriminating features-based cost-sensitive approach for software defect prediction",

abstract = "Correlated quality metrics extracted from a source code repositorycan be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highlyunbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a factthat the selection of the best discriminating features significantly improvesthe robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating featuresthat help in accurately predicting a defect in a software component. Secondly,a cost-sensitive logistic regression and decision tree ensemble-based predictionmodels are applied to the best discriminating features for precisely predictinga defect in a software component. The proposed models are compared withthe most recent schemes in the literature in terms of accuracy, area under thecurve (AUC), and recall. The models are evaluated using 11 datasets and itis evident from the results and analysis that the performance of the proposedprediction models outperforms the schemes in the literature.",

keywords = "AUC, Cost-sensitivity, Discriminating features, Machine learning models, Recall, Software bugs/defects",

author = "Aftab Ali and Naveed Khan and Mamun Abu-Tair and Joost Noppen and McClean, {Sally I} and Ian McChesney",

note = "Funding Information: This research is supported by the BTIIC (BT Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland. Publisher Copyright: {\textcopyright} 2021, The Author(s).",

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AU - Khan, Naveed

AU - Abu-Tair, Mamun

AU - Noppen, Joost

AU - McClean, Sally I

AU - McChesney, Ian

PY - 2021/7/12

Y1 - 2021/7/12

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AB - Correlated quality metrics extracted from a source code repositorycan be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highlyunbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a factthat the selection of the best discriminating features significantly improvesthe robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating featuresthat help in accurately predicting a defect in a software component. Secondly,a cost-sensitive logistic regression and decision tree ensemble-based predictionmodels are applied to the best discriminating features for precisely predictinga defect in a software component. The proposed models are compared withthe most recent schemes in the literature in terms of accuracy, area under thecurve (AUC), and recall. The models are evaluated using 11 datasets and itis evident from the results and analysis that the performance of the proposedprediction models outperforms the schemes in the literature.

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KW - Machine learning models

KW - Recall

KW - Software bugs/defects

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Discriminating features-based cost-sensitive approach for software defect prediction

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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