An application of fuzzy clustering to software quality prediction

Yuan, Xiaojing; Khoshgoftaar, Taghi M.; Allen, Edward B.; Ganesan, K.

doi:10.1109/asset.2000.888052

Cited by 79 publications

(29 citation statements)

References 14 publications

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“…Applying FCM can produce a rational classification by optimally dividing the dataset based on theories and methods of fuzzy mathematics. In recent years, FCM has been successfully applied in such fields as terrain, soil, air quality, water quality, loss of water and soil, land use, etc., which indicates that FCM can be utilized to efficiently represent continuous distribution of physical geographic phenomena in spatial dimensions (Lark 1999;Odeh et al 1992;Wu et al 2004;Yu and Chen 2005;Yuan et al 2001).…”

Section: Methodsmentioning

confidence: 99%

Wind erosion hazard assessment of the Mongolian Plateau using FCM and GIS techniques

Shi

Gao

et al. 2009

Environ Earth Sci

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The most serious environmental problems of the Mongolian Plateau are land degradation and sand storms caused by wind erosion, but the evaluation of wind erosion at regional scales has been a difficult process in wind erosion research. In this study, fuzzy c-means clustering (FCM) was used to assess the spatial pattern of wind erosion hazard on the Mongolian Plateau. By fuzzy clustering four main wind erosion factors (vegetation cover, average degree of land surface relief, degree of soil dryness and intensity of wind energy), wind erosion hazard was classified into six grades. Results show that FCM can effectively integrate related information between wind erosion and environmental factors, which provides the basis for predictive mapping of wind erosion hazard. Spatial patterns of wind erosion hazard indicate a gradual trend of increasing hazard in the Mongolian Plateau from east to west. Similar patterns were also found in NDVI and soil dryness, indicating that soil moisture and vegetation are the most important factors in the formation of wind erosion hazard. In addition, the distribution of different levels of wind erosion hazard is basically consistent with the regional distribution of landscape vegetation types in the Mongolian Plateau.

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Section: Methodsmentioning

confidence: 99%

Wind erosion hazard assessment of the Mongolian Plateau using FCM and GIS techniques

Shi

Gao

et al. 2009

Environ Earth Sci

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“…Khoshgoftaar et al [10] used one hidden layer neural network with backpropagation training algorithm to classify modules as fault prone or not. In their work [12] they used Fuzzy Subtractive Clustering to predict the number of faults. Aggarwal et al [13] have developed a fuzzy model for measuring software maintainability.…”

Section: Related Workmentioning

confidence: 99%

“…Khoshgotaar et al [10,11] introduced the use of ANNs and Fuzzy systems in empirical studies in software engineering. Fuzzy subtractive clustering has been used by them to generate rules for an FIS to predict number of faults in a legacy telecommunication system in the procedural paradigm [12] . Our study is conducted in the OO paradigm where principal components of OO design metrics are input to ANNs, FIS and ANFIS as predictors of maintenance effort.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Approaches for Prediction of Software Maintenance Effort

Kaur¹,

Kaur²,

Malhotra³

2010

IJCA

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The relationship between object oriented metrics and software maintenance effort is complex and non-linear. Therefore, there is considerable research interest in development and application of sophisticated techniques which can be used to construct models for predicting software maintenance effort. The aim of this paper is to evaluate and compare the application of different soft computing techniques -Artificial Neural Networks, Fuzzy Inference Systems and Adaptive Neuro-Fuzzy Inference Systems to construct models for prediction of Software Maintenance Effort. The maintenance effort data of two commercial software products is used in this study. The dependent variable in our study is maintenance effort. The independent variables are eight Object Oriented metrics . It is observed that soft computing techniques can be used for constructing accurate models for prediction of software maintenance effort and Adaptive Neuro Fuzzy Inference System technique gives the most accurate model.

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“…Efficiency is defined by Yuan et al [25] as the proportion of predicted fault-prone modules that are inspected out of all modules.…”

Section: Effectiveness and Efficiencymentioning

confidence: 99%