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

Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

693
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...
693
Response Surface Methodology01:16

Response Surface Methodology

609
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
609
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.1K
Second Derivative Test: Problem Solving01:24

Second Derivative Test: Problem Solving

55
In mathematical analysis, finding a function's highest and lowest points is crucial for understanding its behavior. These points, known as critical points, occur where the first derivative is either zero or undefined. Critical points are potential local maxima and minima locations, which can be classified using the Second Derivative Test. However, not every critical point corresponds to a local maximum or minimum. The second derivative is analyzed to classify these points. The second derivative...
55
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

27.8K
There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
27.8K

You might also read

Related Articles

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

Sort by
Same author

LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.

Scientific reports·2026
Same author

A Quali-Quantitative Analysis of Biosensing and Biotransducing Systems for Cardiovascular Monitoring: Pacemakers Active and Passive Stress.

IEEE transactions on nanobioscience·2025
Same author

Transformative progress of SPIONs in cancer theranostics: A comprehensive review on recent advances in SPIONs technology.

Colloids and surfaces. B, Biointerfaces·2025
Same author

Analysis of Freezing of Gait in Parkinson's Disease Detection Using a Multimodal Prototype Learning Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Design and Analysis of MEMS Pressure Sensor Based on Various Principles of Microcantilever Beam.

IEEE transactions on nanobioscience·2023
Same author

Soil bacterial community structure and functioning in a long-term conservation agriculture experiment under semi-arid rainfed production system.

Frontiers in microbiology·2023

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
05:26

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

1.7K

Design of an iterative adaptive method for volatility-aware test case prioritization in rapidly evolving software

K Srinivasa Rao1,2, A Ananda Rao3, P Radhika Raju4

  • 1Research Scholar, Department of CSE, College of Engineering, JNTUA, Ananthapur, 515002, AP, India.

Methodsx
|September 22, 2025
PubMed
Summary

This study introduces an adaptive framework for Test Case Prioritization (TCP) using deep reinforcement learning. It enhances effectiveness by optimizing test execution order and balancing risk with resource efficiency.

Keywords:
Causal inferenceMulti-objective optimizationProcessReinforcement learningSoftware volatilityTest prioritization

More Related Videos

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.8K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.3K

Related Experiment Videos

Last Updated: Jan 17, 2026

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights
05:26

Author Spotlight: Innovations in iTUG Test for Enhanced Risk Assessment and Cognitive Insights

Published on: October 25, 2024

1.7K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.8K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.3K

Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Test Case Prioritization (TCP) is crucial for efficient software testing.
  • Traditional TCP methods struggle with dynamic software changes and volatility.
  • Optimizing test execution order is key to reducing costs and improving fault detection.

Purpose of the Study:

  • To propose an adaptive, deep reinforcement learning-driven framework for volatility-aware Test Case Prioritization (TCP).
  • To enhance the effectiveness and efficiency of TCP in dynamic software environments.
  • To provide a dependable and understandable TCP solution balancing multiple objectives.

Main Methods:

  • Developed a five-module framework integrating Dual-Attention Temporal Graph Prioritization Network (DAT-GPN), Reinforcement-Driven Volatility-Aware Clustered Prioritizer (RD-VACP), Uncertainty-Regularized Multi-Agent PPO Scheduler (UR-MAPPO), Counterfactual Impact Analysis Prioritizer (CIAP), and Multi-Objective Adaptive Ensemble Prioritization Framework (MO-AEPF).
  • Employed temporal and contextual attention mechanisms, Q-learning, multi-agent PPO with uncertainty regularization, structural causal inference, and ensemble learning.
  • Utilized historical execution logs and software modification data for dynamic graph modeling.

Main Results:

  • The framework effectively addresses volatility-aware optimization for improved TCP.
  • DAT-GPN assigns priority scores using historical data and dynamic graph analysis.
  • RD-VACP optimizes execution order and clusters test cases based on volatility.
  • UR-MAPPO enhances policy stability in dynamic scenarios using uncertainty.
  • CIAP enables risk-aware decision-making through counterfactual analysis.
  • MO-AEPF balances detection time, risk, and resource consumption.

Conclusions:

  • The proposed adaptive framework offers a robust and interpretable solution for Test Case Prioritization.
  • Integration of reinforcement, causal, and sequential learning provides risk-sensitive, optimal execution.
  • Multi-objective ensemble optimization ensures resource efficiency and balanced fault detection.