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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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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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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Related Experiment Video

Updated: Jan 9, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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BugPrioritizeAI for multimodal test case prioritisation using bug reports, code changes, and test metadata.

P Kalyani1, C Prakasa Rao2, Bhargavi Goparaju1

  • 1Department of CSE, Rise Krishna Sai Prakasam Group of Institutions, Ongole, Andhra Pradesh, India.

Scientific Reports
|December 8, 2025
PubMed
Summary

Regression testing is crucial but costly. This study introduces BugPrioritizeAI, an AI-enhanced framework for test case prioritization (TCP) that uses multimodal data for faster fault detection and reduced overhead.

Keywords:
Bug report analysisCode change rankingDeep learningExplainable AITest case prioritisation

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Regression testing is essential in continuous integration and delivery but computationally expensive.
  • Existing test case prioritization (TCP) methods often rely on single data sources and lack semantic understanding.
  • Current deep learning models for TCP face challenges in cross-project generalization and interpretation.

Purpose of the Study:

  • To propose BugPrioritizeAI, an explainable, multimodal framework for test case prioritization.
  • To improve fault detection speed and reduce software testing overhead.
  • To provide developers with insights into test case prioritization decisions.

Main Methods:

  • Developed BugPrioritizeAI, a framework integrating bug reports, source code changes, and test metadata.
  • Utilized a multimodal approach to model semantic relationships across software artifacts.
  • Incorporated an AI-enhanced bug triage component (BugTestRankNet) for ranking potentially buggy files.

Main Results:

  • BugPrioritizeAI effectively ranks test cases for quicker fault detection.
  • The framework demonstrates reduced testing overhead compared to traditional methods.
  • SHAP-based explanations offer transparency into the prioritization logic.

Conclusions:

  • BugPrioritizeAI offers a novel, explainable, and multimodal solution for test case prioritization.
  • The framework addresses limitations of existing TCP approaches and deep learning models.
  • This AI-enhanced method supports efficient software development by optimizing regression testing.