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相关概念视频

Ogive Graph01:07

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Graphing Antiderivatives01:30

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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Updated: Jan 20, 2026

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图表基于神经网络的突变感知回归测试,使用代码依赖图和执行轨迹进行排序.

S Sowmyadevi1, Anna Alphy1

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR campus, Ghaziabad, Utter Pradesh 201204, India.

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|January 19, 2026
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概括
此摘要是机器生成的。

本研究引入了一种使用图形神经网络 (GNN) 的突变感知测试优先级系统,以改进回归测试. 基于GNN的方法显著提高了故障检测和突变覆盖率,优于现有的方法.

关键词:
在APFDD中,APFD是指APFD.故障检测 检测故障检测图形神经网络是一个神经网络.突变测试是为了测试突变.回归测试是一种回归测试.软件质量保证 软件质量保证测试优先级的测试优先级.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 回归测试对于软件质量至关重要,但在有效优先测试方面面临挑战.
  • 现有的测试优先级方法往往难以平衡故障检测,执行成本和覆盖范围.
  • 突变测试虽然有效,但在计算上可能很昂贵,需要复杂的优先级策略.

研究的目的:

  • 使用图形神经网络 (GNN) 开发一种新的突变感知测试优先级系统.
  • 通过整合静态程序结构,动态执行轨迹和突变覆盖来增强回归测试.
  • 为了在测试优先级中实现故障检测,执行成本和突变覆盖之间的卓越平衡.

主要方法:

  • 通过将程序依赖图和调用图与运行时特征相结合,构建混合图表表示.
  • 使用GNN变异 (GCN,GAT,GraphSAGE) 在测试案例中嵌入高阶依赖关系.
  • 使用多目标优化函数根据故障检测,执行成本和突变覆盖范围对测试案例进行排名.

主要成果:

  • 实现了88.9%的平均APFD (检测到故障的平均百分比),明显超过传统基线 (74.5%) 和ML基线 (82.7%).
  • 达到了84.6%的突变得分,最低执行时间为16.1秒.
  • 统计学意义 (Wilcoxon签名等级测试,p < 0.05) 证实了性能增长的稳定性.

结论:

  • 拟议的基于GNN的突变感知测试优先级系统为现代回归测试提供了一个可扩展,准确和突变驱动的范式.
  • 整合执行痕迹和突变特征对于有效的测试优先级是至关重要的,正如废除研究所示.
  • 嵌入GNN可以通过对故障相关的测试案例进行聚类来提供可解释的优先级.