[PDF] Top 20 Optimized Support Vector Machine for Software Defect Prediction
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Optimized Support Vector Machine for Software Defect Prediction
... prevents software from behaving as intended is a software ...quality software product with zero defects. High-risk components in a software project must be caught early to enhance ... See full document
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Software Fault Proneness Prediction Using Support Vector Machines
... Planning and resource allocating for inspection and testing is difficult and it is usually done on empirical basis. The model predicted in the above section could be of great help for planning and executing testing ... See full document
6
Prediction of soil physical properties by optimized support vector machines
... Choosing appropriate values for parameters of SVM is an important step in SVM analysis which has a great in- fluence on its performance and thus on its prediction accu- racy. In this sense, utilization of ... See full document
7
Machine learning Methods for Software Defect Prediction a Revisit
... 5.2 (ANN) Artificial Neural networks: Learning a “Target Function” in Artificial Neural networks (ANN) amounts to discover the weights for a known stable Network construction. Accepted out comes as precise with the ... See full document
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A GENETIC ALGORITHM OPTIMIZED MULTI-LAYER PERCEPTRON FOR SOFTWARE DEFECT PREDICTION
... The reports depend on datasets got from the NASA publicModular toolkit for Data Processing (MDP) repository. This is an open repository for NASA datasets. NASA datasets are made up of many static code attributes. Eight ... See full document
10
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
... sufficiently large, point estimates may be inadequate for choosing the classifier with optimized parameters or features (Reunanen, 2003; Varma and Simon, 2006). For example, in Table 1, we summarize the accuracies ... See full document
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Software Defect Prediction Using Supervised Machine Learning and Ensemble Techniques: A Comparative Study
... of software development is to locate and fix defects ahead of schedule that could be expected under diverse ...Many software development activities are performed by individuals, which may lead to different ... See full document
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Defect Prediction for Object Oriented Software using Support Vector based Fuzzy Classification Model
... In recent past, several classification and prediction models, based on historical defect data sets, have been used for early prediction of error-prone modules.. Considering these facts, [r] ... See full document
9
Software Vulnerability Prediction Using Feature Subset Selection and Support Vector Machine
... the software will be evaluated and V&V activities occur during all phases of the system development ...architecture-based software engineering ...architecture-based software engineering ... See full document
7
Prediction of Software Defect Using Linear Twin Core Vector Machine Model
... A well established metrics program yields to better estimations of cost and schedule. Besides, the analyses of measured metrics are good indicators of possible defects in the software being developed. Testing is ... See full document
6
Software defect prediction using enhanced relevance vector machine
... in software defect prediction [7, ...predict defect-prone ...a defect- count prediction model by taking into account of certain number of process measures along with structural ... See full document
5
An Approach to Efficient Software Bug Prediction using Regression Analysis and Neural Networks
... In software development, early prediction of defective software modules can reduce overall time and budget and increase customer satisfaction [1], by meeting customer requirements to the ...reliable ... See full document
6
Application of Support Vector Machine in Bus Travel Time Prediction
... a support vector machine (SVM) algorithm is proposed based on the measured travel time between bus ...of support vector machine model operation are basically in agreement with ... See full document
5
Online Full Text
... We then compared several methods, including PseAACIndex-Profile, SVM-DT[27], SVM-pairwise, SVM-LA[19], SVM-PDT-Profile[23], BioSVM-2L[22], HHSearch[23], and disPseACC[28], with the SVM-hybrid method. We adopted the same ... See full document
6
A Review on Software Defect Prediction
... improve software quality and productivity. They showed how the various defect prediction models are implemented resulting in reduced magnitude of ...various machine learning techniques for the ... See full document
8
The Fracture Density and Fractal Dimension Prediction Based on Support Vector Machine
... The selection of the parameters of SVR model decides its generalization performance. In a SVR model it contains two sorts of parameters: the basic parameters and kernel function related parameters. Most scholars have ... See full document
8
360° View Camera Based Visual Assistive Technology for Contextual Scene Information
... This chapter gives background information about the methods and algorithms used in this study. We will present algorithms and methods for preprocessing, feature reduction, and classification. The structure of the ... See full document
55
An Optimal Churn Prediction Model using Support Vector Machine with Adaboost
... Customer churn is a common measure of lost customers. By minimizing churn, a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer retention is important ... See full document
6
STUDIES ON IMPROVING TEXTURE SEGMENTATION PERFORMANCE USING GENERALIZED GAUSSIAN MIXTURE MODEL INTEGRATING DCT AND LBP
... This Software Defect Prediction (SDP) holds an important place in the domain of software quality and ...dependability. Software faults may be defined as errors, mistakes or defects in ... See full document
10
Based on Support Vector Machine of Cold Rolling Force Prediction Research
... Bayesian LSSVM predicts the precision of rolling force and determines the precision of rolling force. The deviation model achieves the automatic adjustment of regularization parameters and nuclear parameters, which makes ... See full document
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