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Ranks01:02

Ranks

242
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
190
Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
146
Classification of Systems-II01:31

Classification of Systems-II

150
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
150
Regression Analysis01:11

Regression Analysis

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Design and Analysis for Fall Detection System Simplification
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软件缺陷预测使用学习排名方法.

Ali Bou Nassif1, Manar Abu Talib2, Mohammad Azzeh3

  • 1Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates. anassif@sharjah.ac.ae.

Scientific reports
|November 3, 2023
PubMed
概括

学习排名 (LTR) 有效地预测和排名软件缺陷. 错误计数比错误密度产生更稳定的结果,而特征选择和失衡学习在软件缺陷预测中对LTR没有显著的改进.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 软件缺陷预测 (SDP) 对于优化资源配置和最大限度地降低测试成本至关重要.
  • 项目经理需要对有缺陷的模块进行排名,而不仅仅是识别它们,尤其是在预算限制范围内.
  • 学习排名 (LTR) 是一种适用于SDP的机器学习方法,用于预测和排名有缺陷的模块.

研究的目的:

  • 为SDP进行八个选定的LTR模型的全面比较.
  • 为了评估bug数量与bug密度作为目标变量的影响.
  • 评估不平衡学习和特征选择对SDP中LTR模型性能的影响.

主要方法:

  • 使用故障百分位数平均值对八个LTR模型的实证评估.
  • 使用两个目标变量LTR模型的比较:错误数和错误密度.
  • 分析失衡学习和特征选择技术的影响.

主要成果:

  • 与错误密度相比,错误计数作为排名标准产生了更高的分数和更稳定的结果.
  • 不平衡学习对错误密度预测产生了积极影响,但对错误数量预测产生了负面影响.
  • 功能选择没有显示出对错误密度的显著改善,也没有影响错误数量.

结论:

  • 使用错误计数作为SDP的LTR中的排名标准更有效和更稳定.
  • 不平衡学习和特征选择并不始终提高SDP中的LTR性能,并且可能不会产生优异的结果.