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

Measurement: Standard Units03:38

Measurement: Standard Units

79.4K
Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
79.4K
Measurement: Derived Units03:02

Measurement: Derived Units

55.2K
The International System of Units or SI system, by international agreement, has fixed measurement units for seven fundamental properties: length, mass, time, temperature, electric current, amount of substance, and luminosity. These are called the SI base units.
55.2K
Units of Measurement01:27

Units of Measurement

5.2K
Mechanical engineering is one of the oldest branches of engineering. It deals with designing, analyzing, and manufacturing machines and mechanical systems. To ensure precise and accurate calculations, units of measurement are used. They provide a standard system for expressing and comparing physical quantities.
There are various well-known historical measurement systems, such as the Babylonian system, the Roman system, the Egyptian system, the Olympian system, the British system, and the Indus...
5.2K
International System of Units01:29

International System of Units

5.1K
The International System of Units, known as the SI system, is a universally accepted measurement system recognized and used worldwide. The SI system is based on a set of three base units considered absolute, and their values do not change with location. These base units are meters, kilograms, and seconds.
Prefixes are used to define both larger and smaller quantities in the SI system. For example, milli, micro, and nano define smaller quantities, while kilo, mega, and giga are used to define...
5.1K
Motor Units00:46

Motor Units

61.9K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
61.9K
Motor Units01:13

Motor Units

8.1K
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
8.1K

You might also read

Related Articles

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

Sort by
Same author

Research on Joint Game-Theoretic Modeling of Network Attack and Defense Under Incomplete Information.

Entropy (Basel, Switzerland)·2025
Same author

Association between systemic immune-inflammation index and trimethylamine N-oxide levels in peripheral blood and osteoporosis in overweight and obese patients.

Frontiers in endocrinology·2025
Same author

<i>Dachaihu</i> decoction ameliorates abnormal behavior by regulating gut microbiota in rats with propionic acid-induced autism.

Frontiers in microbiology·2025
Same author

Investigating the role of MicroRNA-519d-3p in enhancing chemosensitivity of colorectal cancer cells to 5-Fluorouracil through PFKFB3 targeting.

Clinics (Sao Paulo, Brazil)·2025
Same author

Mesoscale orchestration of collagen-based hierarchical mineralization.

Nature communications·2025
Same author

Prediction Models for Late-Onset Preeclampsia: A Study Based on Logistic Regression, Support Vector Machine, and Extreme Gradient Boosting Models.

Biomedicines·2025
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

A Technical Guide for Performing Spectroscopic Measurements on Metal-Organic Frameworks
10:13

A Technical Guide for Performing Spectroscopic Measurements on Metal-Organic Frameworks

Published on: April 28, 2023

3.1K

IGTG&R: An Intent Analysis-Guided Unit Test Generation and Refinement Framework.

Xiaojian Liu1, Yangyang Zhang2

  • 1College of Computer Science, Beijing University of Technology, Beijing 100124, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

A new framework, intent analysis-guided unit test generation and refinement (IGTG&R), improves functional defect detection in software testing. This approach combines code coverage-guided unit test generation and large language model-based test generation for superior results.

Keywords:
functional defectsintent analysislarge language modelunit test generation

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.5K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

4.3K

Related Experiment Videos

Last Updated: Jan 29, 2026

A Technical Guide for Performing Spectroscopic Measurements on Metal-Organic Frameworks
10:13

A Technical Guide for Performing Spectroscopic Measurements on Metal-Organic Frameworks

Published on: April 28, 2023

3.1K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.5K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

4.3K

Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Automated Software Testing

Background:

  • Code coverage-guided unit test generation (CGTG) offers high executability but fails to detect functional defects.
  • Large language model-based test generation (LLMTG) excels at intent analysis but suffers from low executability and requires debugging.
  • Existing methods have limitations in comprehensively identifying functional defects while maintaining test quality.

Purpose of the Study:

  • To introduce a novel framework, the intent analysis-guided unit test generation and refinement (IGTG&R) model, to enhance the identification of functional defects in unit test generation.
  • To combine the strengths of CGTG and LLMTG for improved code intent comprehension and defect detection capabilities.
  • To evaluate the effectiveness of the IGTG&R model against traditional CGTG and LLMTG approaches.

Main Methods:

  • A two-stage test generation process is employed within the IGTG&R model.
  • The first stage enhances CGTG using coverage path entropy for improved executability and code coverage.
  • The second stage refines test cases using LLMs for functional defect identification, excluding focal method bodies to reduce interference.

Main Results:

  • The IGTG&R model achieved a functional defect identification rate between 65% and 89%.
  • The model demonstrated a 100% execution success rate and a 75.8% code coverage rate.
  • IGTG&R outperformed both CGTG and LLMTG in identifying functional defects and overall test quality.

Conclusions:

  • The IGTG&R model offers a significant advancement in automated unit test generation by integrating code coverage and intent analysis.
  • The proposed method effectively addresses the limitations of existing approaches, leading to more robust and accurate defect detection.
  • The IGTG&R framework provides a superior solution for software testing, enhancing the ability to identify functional defects and ensure code quality.