Program & Schedule

The program is now available!

FINAL PROGRAM (PDF)
A downloadable PDF version of the complete program, suitable for printing or offline reference.

View Full Schedule and Session Details on Connecto
Interactive schedule with session times, Zoom links, speaker profiles, and venue details for both in-person and virtual attendees.

IEEE AITest 2025 is the seventh edition of the IEEE series conference, focusing on the synergy of artificial intelligence (AI) and software testing. This conference provides an international forum for researchers and practitioners to exchange novel research results, articulate the problems and challenges from practices, deepen our understanding of the subject area with new theories, methodologies, techniques, process models, impacts, etc., and improve the practices with new tools and resources. This year’s conference is scheduled in Tucson, Arizona, USA, from 21-24 July 2025. The conference is part of the IEEE CISOSE 2025 congress.

Topics of Interest

Topics for IEEE AITest 2025 encompass key methodologies for verifying and validating AI systems, along with the innovative use of AI for software testing. They also address the challenges and emerging areas in large language models, data quality, policy, domain-specific AI testing, and the ethical implications of responsible AI development.

For specific topics, please refer to the Call for Paper Page.

News

• Accepted Papers has been updated! – 05/27/2025
• Panel Beyond Accuracy: Engineering Trustworthy AI Systems in Production has been updated! – 05/27/2025
• Tutorial Generative AI Evaluation Essentials has been updated! – 04/28/2025
• The second round of paper submission begins! The deadline is April 30th! –04/09/2025
• The deadline for the first round of paper submission has been extended to April 8th! –03/30/2025
• Workshop Challenges and Innovations in Large Language Models: Reasoning, Architecture, and Beyond has been updated! – 03/26/2025
• Call for Workshops and Tutorials! – 03/01/2025
• Call for Panels! – 03/01/2025
• Call for Papers! – 01/01/2025

Contact

For more information and any questions, please contact the AITest 2025 PC chairs:

• Monowar Bhuyan, monowar@cs.umu.se, Umea University

• Haihua Chen, Haihua.Chen@unt.edu, University of North Texas

To register for IEEE AITest 2025, please visit: https://cvent.me/D1mOer

Hosted by Systems and Industrial Engineering at the University of Arizona, Tucson.
The venue will take place at the University of Arizona Library.

Location

1510 E University Blvd, 85721 Tucson, Arizona, United States

Full Papers (26 in total)

• Tejal Mane and Sourabh Kumbhar, Optimized Social Recommendations Using Graphs and Language Model Integration
• Suryansh Singh Sijwali, Angela Marie Colom, Anbi Guo and Suman Saha, Fixing Performance Bugs Through LLM Explanations
• Alexandra Davidoff, Lynn Vonderhaar, Timothy Elvira and Omar Ochoa, Formal Verification of Synthetic Image Datasets using Large Language Models
• Alexander Günther, Sebastian Vollmer and Peter Liggesmeyer, Single-Valued Risk Estimation in Classification for Dependent Data
• Lynn Vonderhaar, Daniel Machado and Omar Ochoa, Surveying the RAG Attack Surface and Defenses: Protecting Sensitive Company Data
• Nariman Mani and Salma Attaranasl, Enhancing Adaptive Test Healing with Graph Neural Networks for Dependency-Aware Decision Making
• Lilou Sicard-Noel, Eric Wu and Ching Seh Mike Wu, The Power of Patterns in Detecting News Articles Written by AI
• Adya Jha, Ankush Verma and Tanusree De, Rethinking AI Safety and Ethics: A State-of-the-Art Multi-Task Model for Bias and Toxicity Detection via Task-Specific Supervision and Data-Centric Fine-Tuning
• Ben Mitchell and Shikha Shrestha, The Impact of Imputation Timing on Machine Learning Models
• Shivangi Tripathi, Teancy Jennifer Rajesh, Henry Griffith and Heena Rathore, REVISE: A Structured Framework for Paragraph-Level Misinformation Correction in Large Language Models
• Yerang Kim, Denoising Cardiac MRI Images Using Deep Learning
• Morena Barboni, Filippo Lampa, Andrea Morichetta, Andrea Polini and Edward Zulkoski, Mutant-Driven Test Generation for Ethereum Smart Contracts via LLMs
• Behrouz Banitalebi and Satya Venkata Anusha Dwivedula, A Multi-Layer Framework for AI-Driven Quality Control in Large-Scale Data Production
• Suavis Giramata, Madhusudan Srinivasan, Venkat Naidu Gudivada and Upulee Kanewala, Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection
• Chi Chiu So, Yueyue Sun, Jun-Min Wang, Siu Pang Yung, Anthony Wai Keung Loh and Chun Pong Chau, Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
• Pranav Gangwani, Jayesh Soni, Abiel Almonte, Alexander Perez-Pons and Himanshu Upadhyay, Adversarial AI Training to Mitigate Cyber Attacks
• Madhusudan Srinivasan and Jubril Adegboyega Olajuwon, GenFair: Systematic Test Generation for Fairness Fault Detection in Large Language Models
• Direesh Reddy, Vijayalaxmi Methuku and Manan Agrawal, Hybrid LSTM-Transformer Model for Stock Market Prediction: A Deep Learning Approach
• Sidhartha Shukla, Prasad Yacham, Shamnad Mohamed Shaffi, Jinal Mehta, Priyamvada Govil and Gaurav Mittal, Smart Test Plan Generation Using Large Language Models for Utility Systems: A Multi-Component Framework
• Erin Lanus, Brian Lee, Dylan Steberg, Jaganmohan Chandrasekaran and Laura Freeman, CODEX: Testing Machine Learning with the Coverage of Data Explorer Tool
• Sofiane Bessaï, Arnault Ioualalen and Matthieu Martel, Online Detection of Adversarial Examples by Activation Profile Inspection
• Junaid Syed, Ekansh Gupta, Aravind Shankara Narayanan and Matan Carmon, Decoding 3D Geometry: Deep Networks for Mesh Classification
• Tobias Kulmburg and Franz Wotawa, Ontology-Based Testing Revisited
• Madhusudan Srinivasan and Upulee Kanewala, Prioritizing Metamorphic Relations via Execution Profile Dissimilarity for Improved Fault Detection
• Bishal Thapa, Alden Duarte-Vasquez, Sahar Hooshmand and Heena Rathore, Evaluation of Ethical Decision Making in Large Language Models Across Classical Moral Frameworks
• Adiba Mahmud, Yasmeen Rawajfih and Fan Wu, A Systematic Evaluation of AI-Generated Security Patches: Analyzing and Exploiting AI-Generated Fixes

Short Papers (4 in total)

• Sunisha Arora and Bicky Sharma, Investigating Prompt Robustness in Code-Generating Large Language Models: An Empirical Study Across Diverse Prompt Categories
• Ziran Min and Christof Budnik, Verification and Validation of LLM-RAG for Industrial Automation
• Jacob Silva, Henry Griffith and Heena Rathore, Assessing the Ethical Alignment of Large Language Models Using Deep Embedding Models
• Xin Wang and Serene Wang, The Convergence of UX Design and AI: A Scientometric Exploration of Research Fronts

If you have received a paper acceptance e-mail from the PC chairs, please use these author instructions to prepare your final camera-ready manuscript.

All accepted papers must adhere to the IEEE double-column format

IMPORTANT: Do not add any page numbering, header, or footer. If you follow the template, then they will not appear by default.

Page limits:

• regular papers (8 pages)
• short papers (4 pages)

All types of papers can have 2 extra pages, subject to page charges according to the applicable policy. If yes, then you must pay $150 for each extra page during registration. Click here to upload the final manuscript. If you are uploading your paper for the first time into the IEEE CPS system, then you need to create an account in the IEEE CPS system. Thereafter, you need to choose “Begin paper submission” and follow the instructions on the submission system itself. Note that you must complete and submit the copyright form as well.

DEADLINES:

Camera-ready submission: June 15, 2025
Author's registration: June 15, 2025

Note that each Author Registration is valid only for ONE paper. If you have more than one paper, then you need a separate registration for each.

Challenges and Innovations in Large Language Models: Reasoning, Architecture, and Beyond

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Workshop Description

Duration: One day (tentative)
Date: July 23-24, 2025 (tentative)

1. Overview and Motivation

Large Language Models (LLMs), particularly those leveraging Transformer-based architectures, have achieved significant milestones across a variety of NLP tasks such as text generation, summarization, and machine translation. However, these models are not without notable limitations:

• Mathematical Reasoning Challenges: LLMs often display inconsistencies in arithmetic and algebraic reasoning, especially when applying chain-of-thought techniques.
• Architectural Bottlenecks: The Transformer’s self-attention mechanism introduces computational inefficiencies, hindering the effective processing of long sequences.
• Long-Form Generation Difficulties: Generating contextually coherent and factually consistent long-form content remains a key issue due to limitations in memory and context management.

This workshop seeks to unite researchers and industry practitioners to address these challenges. It will focus on sharing innovative research, proposing novel methodologies, and fostering interdisciplinary discussions across NLP, cognitive science, and machine learning.

2. Scope and Objectives

The primary objectives of this workshop are to:

1. Identify Challenges: Explore key limitations in reasoning, generation, and architecture within current LLMs.
2. Investigate Novel Architectures: Highlight emerging models that go beyond traditional Transformers to improve scalability and context handling.
3. Improve Reasoning Methods: Examine advanced reasoning techniques, such as refined chain-of-thought models, to enhance logical and mathematical consistency.
4. Propose Innovative Solutions: Encourage discussions on hybrid systems, retrieval-augmented models, memory-augmented architectures, and efficient computation techniques.
5. Advance Evaluation Benchmarks: Develop or improve metrics and datasets to better assess reasoning, factual accuracy, and long-form coherence.

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Workshop Structure

Format: One-day workshop
Planned Agenda:
- One keynote session
- One technical paper session
- Open Q&A / Panel discussion

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Topical Outline

We invite contributions on (but not limited to) the following topics:

Mathematical Reasoning and Computation

• Enhancing numerical and logical accuracy in LLM outputs
• Techniques for step-by-step reasoning and error mitigation
• Data augmentation and specialized pre-training for mathematical reasoning

Transformer Limitations and Architectural Innovations

• Novel models addressing long-sequence processing (e.g., efficient Transformers, recurrent-transformer hybrids, state-space models)
• Reducing memory and computational overhead in large-scale models
• Enhancing attention mechanisms for improved context retention

Long-Form Generation and Coherence

• Approaches to maintain narrative coherence and factual accuracy in long texts
• Metrics and datasets for evaluating document-level consistency
• Hierarchical and content-planning approaches for structured generation

Interpretability and Debugging of LLMs

• Visualizing model internals: attention, embeddings, and reasoning chains
• Detecting and mitigating hallucinations, biases, and logical fallacies
• Developing user-friendly tools for debugging LLMs

Hybrid Systems and Multimodal Reasoning

• Combining symbolic reasoning, external memory, and neural networks
• Cross-modal models integrating text with visual, audio, or other data modalities
• Applications requiring reasoning over structured and unstructured information

Ethical and Societal Considerations

• Addressing bias, misinformation, and risks in deploying advanced LLMs
• Policy and regulatory frameworks for responsible LLM deployment
• Developing community guidelines and best practices for safe AI usage

Submission

Submission Link

Important Dates

• April 30th, 2025 Submission deadline
• May 10th, 2025 Author’s notification
• June 1st, 2025 Camera-ready and author’s registration

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Anticipated Audience

We anticipate a hybrid event with approximately:

• 20 in-person participants

• 20 online attendees

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Expected Outcomes

• All accepted papers will be published in the official conference proceedings.
• Selected papers will be invited to a special issue of Electronics on “Advances in Generative AI and Computational Linguistics.”

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Short Biographies and Contact Information of the Organizers

Dr. Yang Zhang

Yang Zhang received his Ph.D. in Computer Science from the School of Computing at Macquarie University, Sydney, New South Wales, Australia. Dr. Yang Zhang serves as a Clinical Assistant Professor in the Anuradha and Vikas Sinha Department of Data Science at the University of North Texas, Denton, Texas, USA. Yang’s research primarily focuses on the subdomains of the emerging AI paradigm, with a special emphasis on text mining. Currently, Yang’s research focuses on deploying Large Language Models (LLMs) for sophisticated text processing, with particular attention to Reasoning and Retrieval-Augmented Generation (RAG), which are essential for innovation and efficiency in NLP. His research interests also include AIoT, Web service, Recommender Systems, and Big data privacy and cybersecurity. His research has been published in several recognized venues (e.g., CIKM2024, ICDM2024, UBICOMP2024, and INFOCOM2025).
Email: Yang.zhang@unt.edu

Dr. Taotao Cai

Taotao Cai is a Senior Lecturer in Computing at the University of Southern Queensland (UniSQ). His primary focus is on research in the field of Data Science, including graph data processing, social network analytics, recommendation systems, and complexity science. He completed his Ph.D. degree from Deakin University in 2020. Prior to joining the faculty at UniSQ, Taotao held positions as a Postdoctoral Research Fellow at Macquarie University (Mar 2021 - Jan 2023) and an Associate Research Fellow at Deakin University (July 2020 - Feb 2021). During this time, he made significant research contributions, which have been published in leading international conferences and journals such as IEEE ICDE, Information Systems, and IEEE TKDE.
Email: Taotao.Cai@unisq.edu.au

Dr. Yipeng Zhou

Yipeng Zhou is a senior lecturer with the School of Computing and secretary of the Centre of Frontier AI Research (FAIR), Faculty of Science and Engineering, Macquarie University. Before joining Macquarie University, he was a research fellow with the University of South Australia, and a lecturer with Shenzhen University, respectively. He got his Ph.D. and M.Phil degrees from The Chinese University of Hong Kong, and a B.S. degree from the University of Science and Technology of China. He received the 2023 Macquarie University Vice-Chancellor’s Research Excellence Award and the 2023 IEEE Open Journal of the Communications Society Best Editor Award. He was the recipient of the 2018 Australia Research Council Discover Early Career Researcher Award (DECRA). His research interests lie in federated learning, data privacy-preservation, networking, etc. He has published 130+ papers in top venues, including ICML, INFOCOM, IJCAI, AAAI, TheWebConf, ICNP, ToN, TDSC, JSAC, TPDS, TMC, etc.
Email: yipeng.zhou@mq.edu.au

Dr. Xuan Lu

Xuan Lu is a tenure-track assistant professor at the College of Information Science, at the University of Arizona. Her research focuses on creating novel methodologies of human-centered data science and using them to understand and optimize the activities and outcomes of our future human society, especially those triggered by technological innovations, with a recent emphasis on the domain of the Future of Work. Her work has been published in multiple leading conferences and journals in the field of data science, and she is a recipient of the WWW Best Paper Award (2019) and the Microsoft Research Asia Fellowship (2017).
Email: luxuan@arizona.edu

Dr. Wei Ni

Wei Ni received the B.E. and Ph.D. degrees in Electronic Engineering from Fudan University, Shanghai, China, in 2000 and 2005, respectively. He is a Principal Research Scientist at CSIRO, Sydney, Australia. He is also a Conjoint Professor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University. He serves as a Technical Expert at Standards Australia in support of the ISO standardization of AI and Big Data. Previously, he was a Postdoctoral Research Fellow at Shanghai Jiaotong University (2005–2008); Deputy Project Manager at Bell Labs, Alcatel/Alcatel-Lucent (2005–2008); and Senior Researcher at Devices R&D, Nokia (2008–2009). He has co-authored one book, ten book chapters, more than 300 journal papers, 100+ conference papers, 26 patents, ten standard proposals accepted by IEEE, and three technical contributions accepted by ISO. His research interests include 6G security and privacy, machine learning, stochastic optimization, and their applications to system efficiency and integrity. Dr. Ni has been an Editor of IEEE Transactions on Wireless Communications since 2018, IEEE Transactions on Vehicular Technology since 2022, IEEE Transactions on Information Forensics and Security since 2024, IEEE Communications Surveys and Tutorials since 2024, and Cambridge Press New Research Directions: Cyber-Physical Systems since 2022.
Email: Wei.Ni@data61.csiro.au

Distinguished Professor Michael Sheng

Quan Z. Sheng is a Distinguished Professor and Head of School of Computing at Macquarie University, Sydney, Australia. His research interests include Service-oriented Computing, Distributed Computing, Internet Computing, and the Internet of Things (IoT). Michael holds a Ph.D. degree in computer science from the University of New South Wales (UNSW) and performed his postdoc as a research scientist at CSIRO ICT Centre. He has more than 400 publications. Prof. Sheng is the recipient of the AMiner Most Influential Scholar Award in IoT in 2019, an ARC (Australian Research Council) Future Fellowship in 2014, the Chris Wallace Award for Outstanding Research Contribution in 2012, and the Microsoft Research Fellowship in 2003. He is ranked by Microsoft Academic as one of the Most Impactful Authors in Services Computing (ranked 4th all time) and in Web of Things (Top 20 all time) in 2021. Prof. Sheng is the Vice Chair of the Executive Committee of the IEEE Technical Community on Services Computing (IEEE TCSVC), the Associate Director (Smart Technologies) of Macquarie University Smart Green Cities Research Centre, and a member of the ACS (Australian Computer Society) Technical Advisory Board on IoT.
Email: michael.sheng@mq.edu.au

Generative AI Evaluation Essentials

Abstract

• This half-day tutorial comprehensively explores Generative AI (GenAI) evaluation methodologies, addressing critical challenges in evaluating AI system performance, reliability, and potential risks.
• Participants will dive deep into AI evaluation tools and strategic approaches for testing AI deployed in enterprise, public sector, and academic environments.
• The workshop combines theoretical frameworks with practical insights, covering key topics such as **AI testing strategies, failure mode identification, data collection standards, and continuous performance monitoring. **
• Led by expert practitioners in AI reliability and evaluation, the tutorial offers a unique opportunity to develop advanced skills in navigating the complex landscape of GenAI evaluation.
• The tutorial will provide recommendations for implementing robust evaluation processes for GenAI systems.

Author bios

Dr. Heather Frase

• Head of Veraitech, Program Lead for the AI Risk and Reliability working group at MLCommons, and Senior Advisor for Testing & Evaluation of AI at Virginia Tech’s National Security Institute.
• Her diverse career has spanned significant roles in defense, intelligence, policy, and financial crime.
• She also serves on the OECD Network of Experts on AI and on the board of the Responsible AI Collaborative, which researches and documents AI incidents.

Dr. Sarah Luger

• PhD in Informatics from the University of Edinburgh, specializing in automated question answering. Co-chair of the MLCommons Datasets Working Group.
• Over two decades of expertise in Artificial Intelligence and Natural Language Processing, focusing on human communication challenges. Background includes roles at IBM Watson (Jeopardy! Challenge) and leadership in human computation and AI research communities.
• Recent work includes low-resource machine translation, online toxicity identification, GenAI for marketing, increasing data annotator diversity, and responsible AI.

Dr. Marisa Ferrara Boston

• PhD in Cognitive Science from Cornell University.
• AI professional focused on expert augmentation. Lead scientist of Reins AI and CEO of simthetic.ai, creating processes and datasets to validate enterprise AI use.
• Held leadership roles at Google and KPMG, applying expertise to financial audit, healthcare, marketing, and creativity enhancement.

Duration

Half-day

Tutorial website/repository

For more information, please refer to the tutorial link.

Beyond Accuracy: Engineering Trustworthy AI Systems in Production

Date: July 22nd

Abstract

As AI systems transition from research labs to production environments, the focus must shift from accuracy to trust. In real-world deployments, especially in regulated, customer-facing, or mission-critical contexts, metrics like accuracy and F1 score are no longer enough. This panel brings together senior experts across machine learning, observability, compliance, and product development to share frameworks, failures, and real-world insights on building AI systems that are explainable, auditable, and resilient. Attendees will walk away with tactical knowledge on monitoring drift, balancing innovation with governance, and designing for long-term trust in AI.

Panel Objectives

By attending this panel, participants will:

• Understand why conventional model performance metrics are insufficient in production.
• Learn how to build observability and monitoring into AI systems to detect data/model drift.
• Explore how governance, compliance, and human oversight can be integrated into the AI lifecycle.
• Gain insight into how cross-functional collaboration drives trust in AI outcomes.
• Hear real stories of what worked and what didn’t from panelists building high-stakes AI systems.

Topics Covered

• Rethinking Success Metrics: How to move beyond accuracy and define trust for production systems.
• AI Observability: Monitoring data pipelines, drift, model behavior, and system health.
• Feedback Loops & Drift Management: Keeping AI systems in sync with real-world changes.
• Governance & Risk: Balancing innovation speed with regulatory, ethical, and compliance needs.
• Privacy and Explainability: Building AI that is transparent and respectful of sensitive data.
• Cross-Functional Collaboration: Aligning engineering, product, and data to solve production challenges.
• Lessons from the Field: Real stories of building scalable, resilient, and trusted AI systems under pressure.

Panelists and their bios

Anusha Dwivedula

Anusha Dwivedula is the Director of Product for the Analytics group at Morningstar. She led the design and rollout of Morningstar’s centralized data platform, which unified pipelines, analytics, and observability across the enterprise to support AI readiness at scale. Her work bridges cloud infrastructure, data governance, and AI product development in high-stakes, regulated environments.

Anusha has extensive experience leading global teams and complex modernization initiatives, focusing on building trusted, explainable, and scalable data systems. She is a frequent speaker on topics such as responsible AI, data quality, and observability, having recently engaged at the Women Impact Tech Summit, the Chief AI Officer Forum, and the IEEE CAI Standards Conference. For her leadership and contributions, the INFORMS Chicago Chapter named her “Professional of the Year 2024.”

As the organizer and moderator of this panel, Anusha brings a systems-first perspective and a commitment to bridging technical rigor with real-world impact in deploying trustworthy AI.

Shane Murray

Shane Murray is the Field CTO at Monte Carlo, where he advises data and engineering leaders on building reliable, trustworthy data and AI systems. Previously, he served as SVP of Data & Insights at the New York Times, leading cross-functional teams across data science, analytics, and platform engineering to support digital growth and subscription strategies. With two decades of experience across media, SaaS, and consulting, Shane has worked at the intersection of product, experimentation, and data reliability.

He brings deep expertise in data strategy, observability, experimentation frameworks, and aligning analytics with business outcomes. As a founding member of InvestInData, he also supports early-stage startups focused on the future of data infrastructure. Shane’s unique experience bridging technical leadership, experimentation, and data trust makes him an ideal voice for this panel on building resilient, real-world AI systems that people can rely on.

Stephanie Kirmer

Stephanie Kirmer is a staff machine learning engineer at DataGrail, a company committed to helping businesses protect the privacy of customer data and minimize risk. She has almost a decade of experience building machine learning solutions in industry, and before going into data science, she was an adjunct professor of sociology and higher education administrator at DePaul University. She brings a unique mix of social science perspective and deep technical and business experience to writing and speaking accessibly about today’s challenges around AI and machine learning, and is a regular contributor at Towards Data Science. Learn more at https://www.stephaniekirmer.com

Sharoon Srivastava

Sharoon Srivastava is a Principal Product Manager at Microsoft, where he leads the development of Autonomous AI Agents that are reimagining how B2B sales are done. He specializes in building AI-first products from 0 to 1, with a strong focus on engineering trustworthy AI systems for real-world deployment. His work spans designing autonomous workflows, verifying and validating AI behavior at scale, and addressing practical challenges of using large language models (LLMs) in production environments.

Sharoon has a background in data science and engineering and holds an MBA from the University of Chicago Booth School of Business, where he served as President of the Product Management Club. He is passionate about advancing responsible AI in enterprise applications and frequently shares insights on scaling AI-first products while maintaining reliability, safety, and compliance.

Vrushali Channapattan

Vrushali Channapattan is the Director of Engineering at Okta, where she leads Data and AI initiatives. Passionate about Responsible AI, she is a member of Okta’s cross-functional AI Governance group. With over two decades of experience, she has played a pivotal role in democratizing petabyte-scale data and has influenced the development of major big data technologies, including serving on the Project Management Committee for Open-Source Apache Hadoop. Before joining Okta, she spent over nine years at Twitter, contributing to its evolution from a startup to a public company. Vrushali holds a Master of Science in Computer Systems Engineering from Northeastern University in Boston.

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

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Important Dates

Main paper

• April 1st, 2025 Submission deadline
• April 8th, 2025 first round submission deadline
• April 30th, 2025 second round submission deadline
• May 10th, 2025 first round author’s notification
• May 20th, 2025 second round author’s notification
• June 1st, 2025 Camera-ready and author’s registration

Workshop paper

• May 15th, 2025 Submission deadline
• May 22nd, 2025 Author’s notification
• June 1st, 2025 Camera-ready and author’s registration

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Submission

Page Limits

Submit original manuscripts (not published or submitted elsewhere) with the following page limits:

• regular papers (8 pages)
• short papers (4 pages)
• AI testing in practice (8 pages)
• tool demo track (6 pages).

Content

We welcome submissions of both regular research papers that describe original and significant work or reports on case studies and empirical research and short papers that describe late-breaking research results or work in progress with timely and innovative ideas. The AI Testing in Practice Track provides a forum for networking, exchanging ideas, and innovative or experimental practices to address SE research that directly impacts the practice of software testing for AI. The tool track provides a forum to present and demonstrate innovative tools and/or new benchmarking datasets in the context of software testing for AI.

Formats and Submission Instructions

• All papers must be written in English. Papers must include a title, an abstract, and a list of 4-6 keywords.
• All types of papers can have 2 extra pages subject to page charges.
• All papers must be prepared in the IEEE double-column proceedings format
• Authors must submit their manuscripts via easychair IEEE AI Test 2025 by April 8, 2025, 23:59 AoE. at the latest.

For more information, please visit the conference website. The use of content generated by AI in an article (including but not limited to text, figures, images, and code) shall be disclosed in the acknowledgments section of the submitted article.

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Conference Proceedings & Special Section of SCI journals

• All accepted papers will be published by IEEE Computer Society Press (EI-Index) and included in the IEEE Digital Library.
• The best papers will be invited to submit an extended version (with at least 30% novel content) to the selected special issues (TBA).

Aims and Scope

The 2025 IEEE International Conference on Artificial Intelligence Testing (AITest) is pleased to invite proposals for workshops and tutorials. Workshops are intended to bring together communities of interest, both in established communities and in communities interested in the discussion and exploration of a new or emerging issue. They can range in format from formal, perhaps centering on the presentation of refereed papers, to informal, perhaps centering on an extended roundtable discussion among the selected participants.

Tutorials provide an opportunity to offer in-depth education on a topic or solution relevant to research or practice in AI Testing. They should address a single topic in detail. They are not intended to be venues for commercial product training. Workshops and tutorials can span a half-day or a full day.

Proposal Format

Successful proposals should include:

• The title of the workshop or tutorial
• A description of the workshop/tutorial, including whether it is planned as a half-day or full-day event
• A topical outline of the workshop/tutorial
• Anticipated audience for the workshop/tutorial
• Expected outcomes of the workshop/tutorial
• Short biographies and contact information of the proposers

Submission Guidelines

All submissions must be in English, in PDF format, in the current IEEE double-column proceedings format, and up to 2 pages. Suitable LaTeX, Word, and Overleaf templates are available from the IEEE Website.

All proposals should be submitted by email to:

• Haihua.chen@unt.edu

• monowar@cs.umu.se

(Subject: “IEEE AITest 2025 Workshop/Tutorial proposal”)

Organization and Logistics

All workshops and tutorials are planned as in-person events. However, hybrid events (with at least one organizer present in person) can also be accommodated. Please indicate your plan and preference in your proposal.

Important Dates

All dates are Anywhere on Earth (AoE)

• April 1, 2025 – Deadline for submission
• April 7, 2025 – Notification of acceptance
• May 15, 2025 – Workshop paper submission deadline
• May 22, 2025 – Workshop paper author’s notification
• July 23-24, 2025 – Workshop or Tutorial date (tentative)

Contact

For more information and any questions, please contact the AITest 2025 PC chairs:

• Monowar Bhuyan, monowar@cs.umu.se, Umeå University
• Haihua Chen, Haihua.Chen@unt.edu, University of North Texas

Aims and Scope

Panels at the 2025 IEEE International Conference on Artificial Intelligence Testing (AITest) are intended to draw together communities of interest, including testing AI systems, using AI techniques for software testing, as well as those involving emerging issues or techniques of interest to members of the community at large.

The panels typically last about 60–90 minutes and include an extended round-table discussion among the selected participants and the audience members. All proposals are welcome to suggest panel formats that will engage and inform the audience and, if accepted, AITest 2025 will work to provide appropriate facilities and setups to enable the panel techniques. Panels can be comprised of short position statements followed by discussion or can be structured as conversations that engage audience members from the outset. While topics are open, preference will be given to panels that align with the topics of interest of AITest 2025.

Proposal Format

Submissions should include:

• A statement of goals or learning objectives for the panel
• An outline for the panel topics
• The expected audience and expected number of attendees
• A tentative list of panelists and their bios. Please indicate whether the panelists have already been contacted about the panel.
• A discussion of any engagement techniques that will require specific physical or technical requirements for the local hosts (e.g., part of the speakers being online). Please note that at least one panelist and/or organizer must be physically on site.
• Contact and biographical information about the organizers. (It is possible for organizers to serve as panelists as well, but this is not a requirement.) Note organizers’ prior experience with organizing any similarly themed panel or workshop.

Submission Guidelines

All submissions must be in English, in PDF format, in the current IEEE double-column proceedings format, and up to 2 pages. Suitable LaTeX, Word, and Overleaf templates are available from the IEEE Website.

All proposals should be submitted by email to:

• monowar@cs.umu.se
• Haihua.chen@unt.edu

(Subject: “IEEE AITest 2025 Panel proposal”)

Important Dates

All dates are Anywhere on Earth (AoE)

• April 1, 2025 – Deadline for submission
• April 7, 2025 – Notification of acceptance
• July 23-24, 2025 – Workshop or Tutorial date (tentative)

Contact

For more information and any questions, please contact the AITest 2025 PC chairs:

• Monowar Bhuyan, monowar@cs.umu.se, Umea University
• Haihua Chen, Haihua.Chen@unt.edu, University of North Texas