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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
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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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Related Experiment Video

Updated: Nov 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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Attention based GRU-LSTM for software defect prediction.

Hafiz Shahbaz Munir1, Shengbing Ren1, Mubashar Mustafa1

  • 1Computer Science and Engineering, Central South University, Changsha, China.

Plos One
|March 4, 2021
PubMed
Summary

This study introduces DP-AGL, a novel framework for statement-level software defect prediction (SDP). DP-AGL utilizes attention-based GRU-LSTM to significantly improve the accuracy of identifying software failures.

Related Experiment Videos

Last Updated: Nov 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.7K

Area of Science:

  • Software Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Current software defect prediction (SDP) methods operate at the file, class, or function level, limiting prediction accuracy.
  • Accurate defect prediction is crucial for developing reliable, high-quality software.

Purpose of the Study:

  • To propose a novel framework, DP-AGL, for statement-level defect prediction.
  • To enhance the accuracy of software defect prediction by moving from component-level to statement-level analysis.

Main Methods:

  • Developed a framework (DP-AGL) employing an attention-based GRU-LSTM model for statement-level defect prediction.
  • Utilized clang to construct Abstract Syntax Trees (ASTs) and defined 32 statement-level metrics.
  • Engineered a three-dimensional vector representation for labeled statements, feeding into the GRU-LSTM model with an Attention mechanism.

Main Results:

  • The DP-AGL framework demonstrated improved performance metrics compared to existing methods.
  • Recall, precision, accuracy, and F1-score saw increases of 1%, 4%, 5%, and 2%, respectively.
  • Experiments were validated on a large dataset of 119,989 C/C++ programs from Code4Bench.

Conclusions:

  • Statement-level defect prediction using DP-AGL offers a more accurate approach to identifying software failures.
  • The proposed framework, leveraging attention-based GRU-LSTM and AST metrics, enhances software quality assurance.
  • DP-AGL shows significant potential for improving the reliability of software development processes.