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Dynamic domain analysis for predicting concept drift in engineering AI-enabled software

Journal of Data and Information Science
Volume 10 (2025): Issue 2 (April 2025)
By:
Open Access
|May 2025

Figures & Tables

Figure 1.

Class and co-variant drifts and how our approach tends to eventually address the domain concept drifts in AIS.
Class and co-variant drifts and how our approach tends to eventually address the domain concept drifts in AIS.

Figure 2.

High-level overview of our iterative process.
High-level overview of our iterative process.

Figure 3.

Current research focuses on the prediction of concept drift, while future work aims at addressing the drift.
Current research focuses on the prediction of concept drift, while future work aims at addressing the drift.

Figure 4.

Sudden drift in P(X): The frequency of pedestrian safety-related topics, on March 18, 2018, the day a self-driving Uber ran over a pedestrian in Arizona.
Sudden drift in P(X): The frequency of pedestrian safety-related topics, on March 18, 2018, the day a self-driving Uber ran over a pedestrian in Arizona.

Figure 5.

Model-generated captions and detected objects.
Model-generated captions and detected objects.

Figure 6.

The change in mean similarity scores for “pedestrian” in social topics before, during, and after car accidents.
The change in mean similarity scores for “pedestrian” in social topics before, during, and after car accidents.

Figure 7.

The Gaussian probability density function shows the interval probabilities as areas under the curve.
The Gaussian probability density function shows the interval probabilities as areas under the curve.

Figure 8.

The set of terms and their probability shifts in the car accident data.
The set of terms and their probability shifts in the car accident data.

Figure 9.

The change in mean similarity scores for “pedestrian” in social topics before, during, and after Halloween.
The change in mean similarity scores for “pedestrian” in social topics before, during, and after Halloween.

Figure 10.

The set of terms and their probability shifts in the Halloween data.
The set of terms and their probability shifts in the Halloween data.

Figure 11.

Probability shifts of terms in the Airplane Crashes dataset.
Probability shifts of terms in the Airplane Crashes dataset.

Figure 12.

The change in mean similarity scores for the term “airplane” in social topics before, during, and after the Plane Crash.
The change in mean similarity scores for the term “airplane” in social topics before, during, and after the Plane Crash.

Qualitative comparison of concept drift detection methods_

MetricProposed FrameworkFiCSUMDDM/EDDM
ProactivityHigh (Proactive)Moderate (Recurring Drifts)Low (Post Hoc)
AdaptabilityHigh (Domain-Agnostic)Moderate (Recurring Drifts)Low (Frequent Retraining)
FeatureSemantic + VisualMeta-FeaturesError-Based Only
EfficiencyHighModerateModerate
Detection AccuracyHighHighModerate

Top five words returned by different search queries on Google Books N-gram_

“pedestrian” + [verb]“pedestrian” + [noun]
pedestrian crossingpedestrian traffic
pedestrian walkspedestrian mall
pedestrian killedpedestrian bridge
pedestrian passpedestrian street
pedestrian movingpedestrian zone

Top ten similar words to pedestrian from Wikipedia and Google News corpora_

Wiki termsSimilarityGoogle termsSimilarity
walkway0.6928bicyclist0.6166
lanes0.6808crosswalk0.5942
sidewalks0.6572motorist0.5460
roadway0.6411bike lanes0.5416
vehicular0.6380pedestrian walkways0.5328
thoroughfare0.6337bicycle lanes0.5256
subway0.6296bikeway0.5248
underpass0.6193traffic calming0.5239
overpass0.6157roadway0.5181
parking0.6129traffic0.5173

Collected datasets for autonomous car accidents_

DateAccident# Tweets
29 July 2016Tesla89,881
18 March 2018Uber119,121
26 April 2019Tesla154,916

Summary of the GDELT dataset for airplane crash events_

Airplane CrashDate of CrashTotal News ArticlesArticles Containing “Airplane”
California 2020Jan 26, 20201,813,7103,900 (before: 1,327, during: 1,313, after: 1,260)
Washington 2022Sep 4, 20221,126,8991,979 (before: 576, during: 768, after: 635)
Washington 2025Jan 29, 20251,886,17311,541 (before: 1,437, during: 6,984, after: 3,120)
DOI: https://doi.org/10.2478/jdis-2025-0020 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
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Language: English
Page range: 124 - 151
Submitted on: Nov 15, 2024
Accepted on: Mar 11, 2025
Published on: May 7, 2025
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
AI-enable software system,
Concept drift detection,
Applied machine learning,
Autonomous vehicles,
Natural language processing
Related subjects:
Computer sciences,
Information technology,
Project management,
Databases and data mining

© 2025 Murtuza Shahzad, Hamed Barzamini, Joseph Wilson, Hamed Alhoori, Mona Rahimi, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.

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