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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

630
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
630
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

98
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
98
Multimachine Stability01:25

Multimachine Stability

143
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
143
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

368
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
368
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

489
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
489
Stereotype Content Model02:16

Stereotype Content Model

14.0K
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...
14.0K

You might also read

Related Articles

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

Sort by
Same author

Improved the slow digestion property of maize starch using partially β-amylolysis.

Food chemistry·2014
Same author

Blend-modification of soy protein/lauric acid edible films using polysaccharides.

Food chemistry·2014
Same author

Structure and physicochemical properties of octenyl succinic esters of sugary maize soluble starch and waxy maize starch.

Food chemistry·2014
Same author

[Effects of left renal vein division on postoperative renal function during open repair of abdominal aortic aneurysm].

Zhonghua yi xue za zhi·2014
Same author

Association of four insulin resistance genes with type 2 diabetes mellitus and hypertension in the Chinese Han population.

Molecular biology reports·2014
Same author

Neuroprotective effect of pseudoginsenoside-f11 on a rat model of Parkinson's disease induced by 6-hydroxydopamine.

Evidence-based complementary and alternative medicine : eCAM·2014
Same journal

Epidemiological characteristics of amebiasis in Japan from 2001 to 2022.

PloS one·2026
Same journal

Longitudinal associations of academic stress with eating related patterns, nutrition, somatic indicators, and depressive symptoms in university students: A study protocol.

PloS one·2026
Same journal

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning.

PloS one·2026
Same journal

Insulation failure mapping on power transformer bushing using FRA and electrostatic simulation.

PloS one·2026
Same journal

Enhancing medical Q&A systems with multimodal knowledge graphs and dual-layer attention mechanisms.

PloS one·2026
Same journal

UAMP: Consistent video object segmentation with uncertainty-aware memory propagation.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

714

Software technical debt prediction based on complex software networks.

Bo Jiang1, Jiaye Cen1, Erluan Zhu1

  • 1School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, Zhejiang, China.

Plos One
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Social Network Analysis (SNA) metrics to improve software technical debt prediction (TDP) models. Combining SNA and traditional metrics significantly enhances prediction accuracy, with XGBoost showing the best performance.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
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 12, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

714
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
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
  • Computer Science
  • Data Science

Background:

  • Software technical debt prediction (TDP) models often yield unsatisfactory performance.
  • Existing models primarily rely on technical debt (TD)-related metrics, limiting their effectiveness.
  • Social Network Analysis (SNA) metrics offer a novel perspective by analyzing software elements as a network.

Purpose of the Study:

  • To explore the effectiveness of SNA metrics in enhancing software technical debt prediction.
  • To propose an improved TDP approach by integrating SNA metrics with existing TD-related metrics.
  • To evaluate the performance of machine learning classifiers using a combined metric suite.

Main Methods:

  • Software was modeled as a Class Dependency Network to compute SNA metrics.
  • A Combined Metric Suite (CMS) was created by merging SNA and TD-related metrics.
  • Seven common machine learning classifiers were employed to build TDP models using CMS.

Main Results:

  • The combined metric suite (CMS) significantly improved the performance of existing TDP models.
  • XGBoost demonstrated superior performance among the tested classifiers, achieving an [Formula: see text] of 0.77, an MI ratio of approximately 0.10, and a recall close to 0.87.
  • The relative effectiveness of different metric combinations was analyzed.

Conclusions:

  • Integrating SNA metrics offers a promising avenue for enhancing software technical debt prediction.
  • The proposed approach using CMS and machine learning classifiers provides a more effective method for TDP.
  • Further research can explore different metric combinations and classifiers for optimal results.