We used Random Forests machine learning classifier on two software measurement datasets collected from jEdit open-source text editor project and experiments ...
An outlier detection algorithm based on object-oriented metrics thresholds
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Abstract—Detection of outliers in software measurement datasets is a critical issue that affects the performance of software fault.
Alan and Catal [35] presented an outlier detection algorithm using object-oriented metrics thresholds. In another paper by Suresh et al. [36], the numerical ...
This work used Random Forests machine learning classifier on two software measurement datasets collected from jEdit open-source text editor project and ...
TL;DR: This work used Random Forests machine learning classifier on two software measurement datasets collected from jEdit open-source text editor project ...
Detection of outliers in software measurement datasets is a critical issue that affects the performance of software fault prediction models built based on ...
Dec 1, 2021 · The aim of this paper is to systematically investigate research on software metric threshold calculation techniques.
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Statistical methods for outlier detection is an alternative approach to fault detection based on limit checking with constant or linear thresholds. An outlier ...
May 7, 2024 · To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust ...
Abstract. A practical application of object-oriented measures is to predict which classes are likely to contain a fault. This is contended to be meaningful ...