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相关概念视频

Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

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A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
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相关实验视频

Updated: Jun 18, 2025

Modeling Fetal Alcohol Spectrum Disorders in Zebrafish to Characterize the Impact of an Adverse Embryonic Environment on Adult Social Behavior
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关于通过错误讨论数据来改进SZZ算法

Pooja Rani1, Fernando Petrulio1, Alberto Bacchelli1

  • 1Department of Informatics, University of Zurich, Zurich, Switzerland.

Empirical software engineering
|July 29, 2024
PubMed
概括
此摘要是机器生成的。

纳入错误讨论细节显著提高了SZZ算法的准确性,以识别引入错误的提交. 这种增强有助于通过分析开发者对话中提到的相关文件,更精确地确定软件缺陷.

关键词:
引入错误的承诺会导致错误.经验研究是实证研究.莫西拉 莫西拉 莫西拉 莫西拉 莫西拉提取请求可以提取.在 SZZ 算法中,软件质量 软件质量纳税学是一种分类学.

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Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e
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科学领域:

  • 软件工程 软件工程 软件工程
  • 实证软件工程 实证软件工程
  • 缺陷分析 缺陷分析

背景情况:

  • 软件质量研究通常依赖于历史缺陷数据.
  • SZZ算法是基于代码修改识别引入错误的提交的普遍技术.
  • 由于纠和幽灵提交等问题,现有的SZZ变体在准确性方面扎.

研究的目的:

  • 调查bug讨论内容是否可以提高SZZ算法的准确性.
  • 从错误讨论中识别相关的和外部的文件,以提高SZZ的有效性.
  • 解决当前SZZ方法在确定缺陷来源方面的局限性.

主要方法:

  • 从Mozilla开发人员的手动链接错误报告中获益.
  • 创建了包含 12,472 个错误报告的 RoTEB 数据集.
  • 手动检查了一份错误报告样本,以评估SZZ的文件相关性.
  • 用来自错误讨论的信息来增强SZZ算法,并评估其性能.

主要成果:

  • 定义了一个分类系统,用于开发人员在错误讨论中引用文件.
  • 观察到,bug讨论中经常提到对SZZ有益的文件.
  • 验证了从讨论中整合文件引用可以提高SZZ在确定引入错误的提交时的精度.
  • 没有发现对SZZ召回有重大影响.

结论:

  • 错误讨论为提高SZZ算法精度提供了有价值的信息.
  • RoTEB数据集为未来关于缺陷分析的研究提供了资源.
  • 需要进一步的探索,以有效地解决纠和幽灵的承诺.