Oesophageal pH has a power‐law distribution in control and gastro‐oesophageal reflux disease subjects

Bredenoord, Albert J.; Smout, A. J. P. M.

doi:10.1111/j.1365-2036.2005.02411.x

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Figure 1 shows the distributions of the NOC (number of children of a given class in an inheritance tree) and RFC (number of distinct methods invoked by code in a given class) metrics from an example using the Camel1.6 dataset in the Promise database. science, computer science, biology, ecology, demography and social science, and economy and finance, and has various manifestations [23][24][25][26][27][28][29][30][31].…”

Section: Correlation and Characteristicsmentioning

confidence: 99%

A Novel Approach for Software Defect prediction Based on the Power Law Function

Ren

Liu

2020

Applied Sciences

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Power law describes a common behavior in which a few factors play decisive roles in one thing. Most software defects occur in very few instances. In this study, we proposed a novel approach that adopts power law function characteristics for software defect prediction. The first step in this approach is to establish the power law function of the majority of metrics in a software system. Following this, the power law function’s maximal curvature value is applied as the threshold value for determining higher metric values. Furthermore, the total number of higher metric values is counted in each instance. Finally, the statistical data are clustered into different categories as defect-free and defect-prone instances. Case studies and a comparison were conducted based on twelve public datasets of Promise, SoftLab, and ReLink by using five different algorithms. The results indicate that the precision, recall, and F-measure values obtained by the proposed approach are the most optimal among the tested five algorithms, the average values of recall and F-measure were improved by 14.3% and 6.0%, respectively. Furthermore, the complexity of the proposed approach based on the power law function is O ( 2 n ) , which is the lowest among the tested five algorithms. The proposed approach is thus demonstrated to be feasible and highly efficient at software defect prediction with unlabeled datasets.

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