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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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,
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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.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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相关实验视频

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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基于主题建模的软件缺陷和根源原因的预测,使用BERTopic和多输出分类器.

Devi Priya Gottumukkala1, Prasad Reddy P V G D2, S Krishna Rao3

  • 1Department of CS&SE, TDR-HUB, Andhra University, Visakhapatnam, India. mantena2377@gmail.com.

Scientific reports
|July 14, 2025
PubMed
概括

本研究介绍了BERT-MOC用于软件缺陷预测 (SDP),使用自然语言处理 (NLP) 和机器学习 (ML). 该模型准确预测缺陷及其根本原因,提高软件工程效率.

关键词:
贝尔主题 贝尔主题 贝尔主题多输出分类器 多输出分类器软件缺陷预测 (SDP) 软件缺陷预测

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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
  • 自然语言处理自然语言处理.

背景情况:

  • 软件缺陷在软件工程中构成重大挑战,导致调试和维护成本增加.
  • 现有的软件缺陷预测 (SDP) 方法往往缺乏识别缺陷的根本原因的能力.
  • 自然语言处理 (NLP) 和机器学习 (ML) 的先进技术为改进SDP提供了新的可能性.

研究的目的:

  • 开发软件缺陷预测 (SDP) 的先进方法,同时识别缺陷及其根本原因.
  • 利用基于变压器的主题建模和多输出分类来进行增强的缺陷分析.
  • 提高软件开发中缺陷解决的效率和准确性.

主要方法:

  • 拟议的BERT-MOC方法集成BERTopic用于缺陷描述的主题建模和多输出分类器.
  • BERTopic从文本缺陷数据中提取有意义的主题表示,以确定缺陷的根本原因.
  • 一个多输出分类器,利用诸如逻辑回归,决策树,K邻居,随机森林和集合方法投票等估计器,在组合主题表示和缺陷日志上进行训练.

主要成果:

  • 在预测缺陷存在和根本原因方面,BERT-MOC模型取得了很高的表现.
  • 采用集体方法投票的多输出分类器表现出卓越的结果,达到97%的准确性和F1得分,用于根源原因预测.
  • 该模型还获得了94%的准确性和F1评分,用于预测缺陷或没有缺陷.

结论:

  • 伯特-MOC方法提供了一种新且有效的方法来预测软件缺陷和分析根本原因.
  • 将NLP主题建模与多输出ML分类集成,可以显著提高缺陷预测的准确性.
  • 这种方法有可能大大减少软件工程中的调试和维护工作.