Fuzzy Clustering of Open-Source Software Quality Data: A Case Study of Mozilla

We present a fuzzy cluster analysis of software quality data extracted from the Mozilla open-source Web browser. This is a new dataset that combines object-oriented software quality metrics with the number of defects per code unit. We undertake a fuzzy cluster analysis of this dataset, which for the...

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Vydané v:The 2006 IEEE International Joint Conference on Neural Network Proceedings s. 4089 - 4096
Hlavní autori: Dick, S., Sadia, A.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 2006
Predmet:
Algorithm design and analysis
Computer aided software engineering
Computer bugs
Data mining
Fuzzy clustering
Object-oriented software
Open source software
Pareto analysis
Quality management
Software engineering
Software metrics
Software quality
ISBN:9780780394902, 0780394909
ISSN:2161-4393
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Abstract We present a fuzzy cluster analysis of software quality data extracted from the Mozilla open-source Web browser. This is a new dataset that combines object-oriented software quality metrics with the number of defects per code unit. We undertake a fuzzy cluster analysis of this dataset, which for the first time addresses the use of both hyperspherical and hyperellipsoidal fuzzy clusters (using the Gath-Geva algorithm) in software quality analysis. Using a Pareto analysis based on the fuzzy clusters, we were able to identify groups of modules having higher defect densities than would be found by merely ranking modules based on any single software metric.
AbstractList We present a fuzzy cluster analysis of software quality data extracted from the Mozilla open-source Web browser. This is a new dataset that combines object-oriented software quality metrics with the number of defects per code unit. We undertake a fuzzy cluster analysis of this dataset, which for the first time addresses the use of both hyperspherical and hyperellipsoidal fuzzy clusters (using the Gath-Geva algorithm) in software quality analysis. Using a Pareto analysis based on the fuzzy clusters, we were able to identify groups of modules having higher defect densities than would be found by merely ranking modules based on any single software metric.
Author Sadia, A.
Dick, S.
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SubjectTerms Algorithm design and analysis
Computer aided software engineering
Computer bugs
Data mining
Fuzzy clustering
Object-oriented software
Open source software
Pareto analysis
Quality management
Software engineering
Software metrics
Software quality
Title Fuzzy Clustering of Open-Source Software Quality Data: A Case Study of Mozilla
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