Software Testing for AI (SE4AI) Professors and Supervisors

Fabio Calefatoمشاهده پروفایل

دانشیار

University of BariItaly

Fabio Calefato is an Associate Professor at the Department of Computer Science, University of Bari "A. Moro" since 2022, holding national habilitation as Full Professor in academic disciplines 01/B1 (since Dec. 2023) and 09/H1 (since Mar. 2025). He serves as CEO & co-founder of PeoplewareAI academic spin-off (2021-present), Rector's delegate of GARR network (2023-present), and Director's delegate for student traineeships (2023-present). Previously, he was Assistant Professor at the Department of Computer Science (2019-22) and at the Jonian Department, Taranto (2015-19), and conducted post-doctoral research at the Department of Computer Science (2007-08; 2010-14). Dr. Calefato received his Ph.D. in Computer Science in May 2007 from the University of Bari with a thesis titled "Supporting Synchronous Communication in Distributed Software Teams," which was listed on SIGSOFT Selected Ph.D. Dissertations in Software Engineering and published by VDM Publishing. He also holds an MSc in Computer Science from the University of Bari (2002). His research focuses on collaborative and social software engineering, mining software repositories and technical Q&A sites, SE4AI, psychometric and sentiment analyses from communication channels in the social programmer ecosystem, collaboration in online creative communities, community-driven knowledge sharing, and computer-mediated communication theories and tools. His work bridges software engineering with social psychology and AI, particularly examining how developers interact, collaborate, and express emotions in online platforms. Analysis of his recent publications reveals a strong trajectory toward AI integration in software engineering practices, with increasing focus on AutoML applications, sentiment analysis in developer communities, and the impact of generative AI on open-source development. His research spans both theoretical foundations and practical tools, with several software implementations including EMTk (Emotion Mining Toolkit), Apache-Big5 (developer personality profiler), and Pynblint (Jupyter notebook quality assurance). Best Paper Award at ICGSE'18 SIGSOFT Selected Ph.D. Dissertations in Software Engineering Dr. Calefato actively advises doctoral students, with Kelly Azevedo Borges Leal (current, researching AutoML for Software Engineering), Luigi Quaranta (graduated 2022, thesis on collaboration around computational notebooks), and Giuseppe Iaffaldano (graduated 2020, thesis on dynamics of online collaborative communities). His research has been supported through various grants enabling international collaborations with institutions including Carnegie Mellon University, University of Victoria, and TU Wien. He leads the COLLAB research group at the University of Bari, which develops tools and conducts empirical studies on collaborative software development. The group maintains several active GitHub projects including Apache-Big5, mini-IPIP test, EMTk, and pySOreputation, alongside experimental datasets used in multiple published studies. His academic spin-off PeoplewareAI focuses on applying AI to human aspects of software development.

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Gail E. Kaiserمشاهده پروفایل

استاد

National and Kapodistrian University of AthensGreece

Gail E. Kaiser is a Professor of Computer Science at Columbia University, where she conducts research in software engineering and security from a systems perspective. Her work spans program analysis, software testing, and pioneering applications of AI to software engineering (AI4SE) and vice versa (SE4AI), with significant contributions dating back to the 1980s. Education: PhD from Carnegie Mellon University ScB from Massachusetts Institute of Technology Research Focus: Professor Kaiser's work centers on static and dynamic program analysis , software testing , and software security . She pioneered applying software engineering testing techniques (particularly metamorphic testing) to machine learning software (SE4AI) during her 2005-2006 sabbatical at Columbia's Center for Computational Learning Systems. Her historical contributions include semantics-focused language-based editors (1980s-1990s, precursors to modern IDEs) and self-adaptation techniques for cloud computing (late 1990s-2000s). Publication Trends: Recent publications (2020-2024) reveal intense focus on AI-software engineering intersections: developing code generation/refinement systems (CYCLE), cross-lingual code search models (REINFOREST), execution-aware pre-training (TRACED), and specialized testing for deep learning systems. Her work consistently bridges traditional program analysis with cutting-edge AI methodologies to solve complex software quality challenges. Academic Service: Kaiser maintains active leadership in the research community through program committee roles at top conferences including PLDI, ICSE, ESEC/FSE, ASE, and SPLASH from 2013-2025. Her GitHub profile (gailkaiser) hosts course materials like COMS W4156 Advanced Software Engineering, demonstrating ongoing educational impact.

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Valerio Terragniمشاهده پروفایل

مدرس ارشد

Max Planck Institute for Security and PrivacyGermany

Valerio Terragni is a Senior Lecturer in the Department of Electrical, Computer, and Software Engineering at the University of Auckland, New Zealand, where he also serves as the Program Director of the Software Engineering Degree. His position as Senior Lecturer in New Zealand is equivalent to Associate Professor in many Asian, European, and North American academic systems. His educational background includes a PhD in Computer Science from The Hong Kong University of Science and Technology (2017), and B.Sc. and M.Sc. degrees in Computer Science from the University of Milano-Bicocca, Italy. Prior to his current position, he was a Senior Research Fellow at Università della Svizzera italiana (USI) in Lugano, Switzerland from 2017 to 2020. Dr. Terragni's research focuses primarily on Software Testing, with special emphasis on automated techniques for generating test cases and their applications in modern software systems. His current work centers on SE4AI (Software Engineering for AI) and AI4SE (AI for Software Engineering), particularly investigating the intersection of software testing and large language models. His research has been published in leading software engineering venues including IEEE TSE, ACM TOSEM, ICSE, FSE, ASE, ICST, and ISSTA. His recent publications demonstrate a strong focus on metamorphic testing, particularly for AI systems and large language models, as well as work on test oracle generation, software quality assessment, and software engineering education. His research shows a clear evolution from traditional software testing techniques toward addressing the unique challenges posed by AI systems. As Program Director of the Software Engineering Degree at the University of Auckland, Dr. Terragni plays a significant role in shaping software engineering education. His work on GradeStyle demonstrates his commitment to improving programming education through automated assessment tools integrated with GitHub.

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Foutse Khomhمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Foutse Khomh is a Full Professor at Polytechnique Montréal, holding multiple prestigious chairs including Canada Research Chair Tier 1, Canada CIFAR AI Chair, and FRQ-IVADO Research Chair. He leads the SWAT Lab (Software Analysis and Testing) at Polytechnique Montréal and has established himself as a leading researcher in software engineering for AI systems. Dr. Khomh received his Ph.D. in Software Engineering from the University of Montreal in 2011. His research has been recognized with numerous awards including four ten-year Most Influential Paper Awards and eight Best/Distinguished Paper Awards. He was also awarded the CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019. His research focuses on software maintenance and evolution, cloud engineering, machine learning systems engineering, empirical software engineering, software analytics, and dependable and trustworthy AI/ML. Dr. Khomh has published over 200 conferences and journal papers, demonstrating his prolific contribution to the field of software engineering, particularly in the context of AI and machine learning systems. His recent publications show a strong emphasis on trustworthy AI, debugging deep learning systems, and addressing challenges in LLM-generated code. Four ten-year Most Influential Paper Awards Eight Best/Distinguished Paper Awards CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize (2019) Canada Research Chair Tier 1 Canada CIFAR AI Chair FRQ-IVADO Research Chair NSERC Arthur B. McDonald Fellow Dr. Khomh has mentored numerous students and researchers in the field, serving as a key advisor in the development of next-generation software engineering researchers. He has also secured significant research funding through his various chairs and fellowships. His leadership extends to major research initiatives including the DEpendable Explainable Learning (DEEL) project and the NSERC CREATE SE4AI training program, which focus on developing trustworthy AI systems and training future AI software engineers. He leads the SWAT Lab (Software Analysis and Testing Lab) at Polytechnique Montréal and is actively involved in the Confiance.ia Quebec initiative on Trustworthy AI, which aims to establish best practices for trustworthy AI development. His lab focuses on empirical studies of software systems, particularly those involving machine learning components, and develops tools and techniques for improving software quality and reliability.

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Fabio Palombaمشاهده پروفایل

دانشیار

University of GenoaItaly

Fabio Palomba is an Associate Professor at the Department of Computer Science , University of Salerno, Italy. He earned a European PhD in Management & Information Technology (2017), funded by University of Salerno and University of Molise, under advisor Prof. Andrea De Lucia. His research spans software maintenance and evolution , empirical software engineering , and ML systems quality . Recipient of IEEE Computer Society Best PhD Thesis Award (2017) Multiple Distinguished Paper Awards from ACM/SIGSOFT and IEEE/TCSE Recipient of prestigious SNSF Ambizione grant (2019) and IEEE Rising Star Award (2023) His work investigates fairness-aware practices in ML , technical debt in AI systems , and LLM applications in software engineering . Recent studies focus on automated requirements generation via RECOVER, quantum software engineering , and socio-technical community smells in ML-enabled systems, with empirical analyses across large datasets. Key editorial roles include Elsevier's Information and Software Technology Journal (2022-), Springer's Empirical Software Engineering Journal (2021-), and IEEE Transactions on Software Engineering (2020-). He has served as program co-chair for SANER 2024 , ICPC 2021 , and multiple conference tracks. 16 Distinguished Reviewer Awards for his refereeing work Co-authored 80+ journal papers , 100+ conference papers , and advised 300+ theses

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Xiang Gaoمشاهده پروفایل

دانشیار

Max Planck Institute for Security and PrivacyGermany

Xiang Gao is a Pre-tenure Associate Professor in the School of Software at Beihang University, China. His research focuses on applying program analysis, test generation, and formal methods to improve software quality through automated bug fixing and program synthesis. He has established significant collaborations with Fujitsu Laboratories of America, Microsoft Research, and other leading institutions in the software engineering field, demonstrating strong industry-academia connections. Dr. Gao received his Bachelor's degree in Computer Science (Elite Class) from Shandong University in 2016, followed by a Ph.D. from the School of Computing at the National University of Singapore, where he also served as a Postdoctoral Fellow until December 2021. His educational background spans both Chinese and Singaporean academic institutions, providing him with a global perspective on software engineering research. His primary research interests span multiple cutting-edge areas of software engineering: Program Analysis techniques for detecting and fixing software bugs with formal methods Software Security vulnerabilities with focus on automated repair methods Automated Program Repair systems that generate high-quality patches without overfitting Program Synthesis for creating transformation rules from examples Software Engineering for Artificial Intelligence (SE4AI) to improve AI model reliability and security Mobile Software Engineering with particular attention to UI testing and automation Deep Learning Security including model protection and obfuscation techniques Dr. Gao's recent publication trajectory shows a strategic evolution toward integrating large language models with traditional software engineering approaches, particularly in test generation and program repair. His work on DNN modularization (NeMo, CNNSpliter, SeaM) represents an innovative approach to enhancing model reusability and security in resource-constrained mobile environments, addressing critical challenges in deploying AI on edge devices. His scientific contributions have been recognized with multiple prestigious awards: ACM SIGSOFT Distinguished Paper Award for "ProveNFix: Temporal Property guided Program Repair" at FSE'24 IEEE TCSE Distinguished Paper Award for "Investigating and Detecting Silent Bugs in PyTorch Programs" at SANER'24 ACM SIGSOFT Distinguished Paper Award for "Modularizing while Training: A New Paradigm for Modularizing DNN Models" at ICSE'24 Distinguished Artifact Award for "Automated Patch Backporting in Linux (Experience Paper)" at ISSTA'21 Dr. Gao actively mentors students at various levels, seeking "self-motivated Ph.D, master, undergraduate students and interns with strong programming skills" for his research projects. He serves on numerous program committees for top software engineering conferences including ICSE, ASE, ISSTA, and FSE, demonstrating his growing influence in the academic community. His research has been supported through collaborations with industry partners including Microsoft Research and Fujitsu Laboratories of America, translating theoretical advances into practical applications. His laboratory focuses on several key research projects including Automated Software Vulnerability Repair (with techniques like Fix2Fit, VulnFix, and ExtractFix that address the overfitting problem in program repair), Program Synthesis for Program Transformation (including Semi-supervised synthesis and FixMorph for automated patch backporting in Linux), and Software Engineering for Artificial Intelligence (with projects like CNNSpliter, SeaM, and Sensei that apply software engineering principles to improve AI model usability and robustness). These projects represent cutting-edge work at the intersection of traditional software engineering and modern AI techniques, addressing critical challenges in software reliability and security.

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Zhou Yangمشاهده پروفایل

استادیار

Max Planck Institute for Security and PrivacyGermany

Zhou Yang is an Assistant Professor at the University of Alberta and Fellow at the Alberta Machine Intelligence Institute (Amii), with research focusing on the intersection of software engineering and artificial intelligence. His academic journey includes a Ph.D. from Singapore Management University, an M.Sc. in Software System Engineering from University College London, and undergraduate studies at Yangzhou University. His research interests span Software Engineering, Artificial Intelligence, AI for Software Engineering (AI4SE), Software Engineering for AI (SE4AI), Large Language Models, and Cybersecurity. Yang's work explores how human and AI collaboration can improve code writing, how AI impacts open-source communities, and how developers build software in emerging environments like VR/AR. His recent publications demonstrate strong trends in code language models, with significant contributions to ASE, ICSE, ISSTA, and top journals like TOSEM and TSE. His research addresses critical challenges including token efficiency in code generation, user perception of AI coding assistants, privacy preservation in code models, and fairness in AI systems. 2024 IEEE Computer Society Best Paper Award (1 out of 183 submissions) ACM Distinguished Paper Award from ISSTA 2024 Distinguished Reviewer Award from Internetware 2024 SMU Research Staff Excellence Award (1 of 4 university-wide) SMU Presidential Fellowship Award SMU Dean's List Award 1st Place in ACM Student Research Competition at ICSE 2024 Yang actively mentors graduate students with regular one-on-one meetings, constructive feedback, and support for top-venue publications. He provides full funding through teaching assistantships and research grants, including travel support for conferences. His lab focuses on responsible research conduct and societal implications of AI work. Though early-career, he actively builds professional networks for students through collaborations and supports diverse career paths in academia and industry. He leads research in the Alberta Machine Intelligence Institute, focusing on practical applications where software engineering principles enhance AI systems and where AI techniques solve real-world software engineering challenges.

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Xiaoning Duمشاهده پروفایل

مدرس ارشد

Max Planck Institute for Security and PrivacyGermany

Xiaoning Du is a Senior Lecturer (equivalent to U.S. Associate Professor) at the Department of Software Systems and Cybersecurity within the Faculty of Information Technology at Monash University, Australia. She was promoted to this position effective July 1, 2025, having previously served as a Lecturer (Assistant Professor) since joining Monash in February 2021. Her research bridges the gap between theory and practical applications of program analysis and formal methods in evaluating traditional and AI-assisted software systems. Dr. Du's educational background includes: PhD from Nanyang Technological University (2020) Bachelor's degree from Fudan University (2014) Dr. Du specializes in software engineering, artificial intelligence, and cybersecurity , with particular focus on SE4AI (Software Engineering for AI), software analysis and testing . Her research has made significant contributions to the security and quality assurance of intelligent software systems, especially intelligent software engineering tools. She is best known for her work on Devign , BigCodeBench , DeepStellar , and SimPy , which have advanced the fields of code generation, program analysis, and AI security. Her approach consistently bridges theoretical foundations with practical applications to improve software quality and security. Dr. Du's recent publications demonstrate a strong focus on the intersection of software engineering and AI, particularly examining how large language models interact with source code. Her work addresses critical challenges in code generation efficiency, security vulnerabilities in AI-assisted development, and fairness in AI systems. She has made significant contributions to benchmarking frameworks like BigCodeBench and has pioneered research on watermarking techniques to protect code datasets from misuse by neural code completion models. Dr. Du has received numerous prestigious awards and recognitions: 2024 Google Research Scholar Award in Software Engineering ACM SIGSOFT Distinguished Paper Award (ISSTA'24) ICLR Oral presentation (2025) 2024 FIT Dean's Early Career Researcher of the Year Award Multiple FIT ECR Seed Grants (2021-2023) Dr. Du actively mentors PhD students, with notable successes including Terry (2024-2025 IBM PhD Fellowship Award recipient) and Zhensu (2024 Bytedance Scholarship Award recipient). She is currently seeking self-motivated PhD students with strong programming skills and relevant research experience, offering full scholarship support. Her research has been supported by multiple grants including the Google Research Scholar Program award and several FIT ECR Seed Grants that have enabled her team to pursue innovative research in software security and AI-assisted development. Dr. Du leads a research group focused on intelligent software systems security and quality assurance. Her team has developed several influential tools and benchmarks including Devign, BigCodeBench, DeepStellar, and SimPy. These resources have become important assets for researchers and practitioners working at the intersection of software engineering and artificial intelligence, particularly in the areas of code generation, program analysis, and security testing of AI systems.

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