Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Variation01:19

Variation

6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.8K
Regression Analysis01:11

Regression Analysis

5.7K
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:
5.7K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The AI implementation gap in trauma radiography: standalone versus discretionary AI-integrated fracture detection.

European radiology experimental·2026
Same author

Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation.

Radiology advances·2026
Same author

A Large-Scale Collection Of (Non-)Actionable Static Code Analysis Reports.

Scientific data·2025
Same author

A rare cause of abdominal pain in pregnancy - mesenteric artery thrombosis and miscarriage in a 34-year-old patient.

International journal of emergency medicine·2024
Same author

CrySyS dataset of CAN traffic logs containing fabrication and masquerade attacks.

Scientific data·2023
Same author

Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions.

Scientific reports·2023

Related Experiment Video

Updated: Jun 16, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Development of partial least squares regression with discriminant analysis for software bug prediction.

Róbert Rajkó1,2, István Siket3,2, Péter Hegedűs3,2

  • 1E-Dimension Ltd., Szeged, Hungary.

Heliyon
|August 21, 2024
PubMed
Summary

This study introduces Partial Least Squares with Discriminant Analysis (PLS-DA) for predicting bug-prone Java classes using source code metrics. The novel approach shows superior performance and faster training compared to existing methods.

Keywords:
Bug predictionPLS-DAPartial least squaresStatic source code metrics

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Related Experiment Videos

Last Updated: Jun 16, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Area of Science:

  • Software Engineering
  • Machine Learning
  • Chemometrics

Background:

  • Predicting bug-prone code is crucial for software maintenance.
  • Existing models using static and historical metrics have limitations.
  • Novel approaches are needed for accurate bug prediction.

Purpose of the Study:

  • To apply Partial Least Squares with Discriminant Analysis (PLS-DA), a chemometric technique, for predicting bug-prone classes in Java programs.
  • To evaluate the performance of PLS-DA against state-of-the-art bug prediction models.
  • To assess the efficiency and completeness of the PLS-DA model.

Main Methods:

  • Utilized static source code metrics of Java programs.
  • Employed Partial Least Squares with Discriminant Analysis (PLS-DA) for classification.
  • Compared PLS-DA performance with existing bug prediction approaches using rigorous statistical evaluation.
  • Investigated model performance with and without data re-sampling techniques.

Main Results:

  • PLS-DA achieved superior performance (F-measure of 0.44-0.47) compared to state-of-the-art methods without data re-sampling.
  • Model training was significantly faster with PLS-DA, simplifying parameter optimization.
  • PLS-DA demonstrated superior completeness, identifying 69.3% (no re-sampling) and 79.4% (up-sampling) of total bugs.

Conclusions:

  • Partial Least Squares with Discriminant Analysis (PLS-DA) is a promising and efficient approach for bug-prone class prediction in software engineering.
  • The chemometric technique offers significant advantages in terms of speed and bug detection completeness.
  • Further research into chemometric methods for software maintenance is warranted.