Performance Comparison of Various Algorithms During Software Fault Prediction

被引：2
|
作者：
Khanna, Munish [1 ]
Toofani, Abhishek [2 ]
Bansal, Siddharth [2 ]
Asif, Mohammad [2 ]
机构：
[1] Hindustan Coll Sci & Technol, Dept Comp Sci & Engn, Mathura, India
[2] Hindustan Coll Sci & Technol, Mathura, India
来源：
INTERNATIONAL JOURNAL OF GRID AND HIGH PERFORMANCE COMPUTING | 2021年 / 13卷 / 02期
关键词：
Adaptive Neuro Fuzzy Inference System (ANFIS); Genetic Algorithm (GA); k Nearest Neighbor (KNN); Logistic Regression; Multi-Layer Perceptron (MLP); Particle Swarm Optimization (PSO); Support Vector Machine (SVM); MACHINE LEARNING-METHODS;
D O I：
10.4018/IJGHPC.2021040105
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Producing software of high quality is challenging in view of the large volume, size, and complexity of the developed software. Checking the software for faults in the early phases helps to bring down testing resources. This empirical study explores the performance of different machine learning model, fuzzy logic algorithms against the problem of predicting software fault proneness. The work experiments on the public domain KC1 NASA data set. Performance of different methods of fault prediction is evaluated using parameters such as receiver characteristics (ROC) analysis and RMS (root mean squared), etc. Comparison is made among different algorithms/models using such results which are presented in this paper.
引用
收藏
页码：70 / 94
页数：25
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