Conference Information

AITest 2025: IEEE International Conference On Artificial Intelligence Testing

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Submission Date:
2025-04-30
Notification Date:
2025-05-20
Conference Date:
2025-07-21
Location:
Tucson, Arizona, USA
Years:
7
Viewed: 26380   Tracked: 2   Attend: 0

Conference Partner Index (CP-I)

46.0 / 100
Ranked #2,542 of 5,682 conferences · Top 45%

#275 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
56
Community attention (10%)
21
Public record completeness (15%)
35

Inputs used: Editions on record: 7 · Researchers following it here: 2 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-15

Call For Papers

AITest 2025 (IEEE International Conference On Artificial Intelligence Testing) is an academic conference held in Tucson, Arizona, USA on 2025-07-21. The paper submission deadline is 2025-04-30. Acceptance notifications are sent on 2025-05-20.

Topics of Interest Topics of interest include, but are not limited to: 1. Testing of AI Methodologies, theories, techniques, and tools for testing, verification, and validation of AI Test Oracle for testing AI Tools and resources for automated testing of AI Techniques for testing deep neural network learning, reinforcement learning, and graph learning 2. AI for Software Testing AI techniques to software testing AI applications to software testing Human testers and AI-based testing Crowdsourcing and swarm intelligence in software testing Genetic algorithms, search-based techniques, and heuristics to optimize testing Constraint programming for test case generation and test suite reduction Constraint scheduling and optimization for test case prioritization and test execution scheduling 3. Large Language Models (LLMs) Testing of Large Language Models (LLMs) Quality evaluation and assurance for LLMs LLMs for software engineering and testing Fairness, ethics, bias, and trustworthiness for LLM applications 4. Data Quality and Policy Data quality and validation for AI Quality assurance for unstructured training data Large-scale unstructured data quality certification AI and data management policies 5. Domain-Specific Testing Specific concerns of testing with domain-specific AI Computer Vision Testing Intelligent Chatbot Testing Smart Machine (Robot/AV/UAV) Testing Impact of GAI on education Responsible AI testing
Last updated by Dou Sun on

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